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Sample records for um modelo neuro-fuzzy

  1. A neuro-fuzzy inference system for sensor monitoring

    International Nuclear Information System (INIS)

    Na, Man Gyun

    2001-01-01

    A neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods has been developed to monitor the relevant sensor using the information of other sensors. The paramters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors

  2. Neuro-fuzzy system modeling based on automatic fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    Yuangang TANG; Fuchun SUN; Zengqi SUN

    2005-01-01

    A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.

  3. Neuro-fuzzy modelling of hydro unit efficiency

    International Nuclear Information System (INIS)

    Iliev, Atanas; Fushtikj, Vangel

    2003-01-01

    This paper presents neuro-fuzzy method for modeling of the hydro unit efficiency. The proposed method uses the characteristics of the fuzzy systems as universal function approximates, as well the abilities of the neural networks to adopt the parameters of the membership's functions and rules in the consequent part of the developed fuzzy system. Developed method is practically applied for modeling of the efficiency of unit which will be installed in the hydro power plant Kozjak. Comparison of the performance of the derived neuro-fuzzy method with several classical polynomials models is also performed. (Author)

  4. A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations Um sistema neuro-difuso para auxiliar no diagnóstico de eventos epilépticos e eventos não epilépticos utilizando diferentes operações aritméticas difusas

    Directory of Open Access Journals (Sweden)

    Lucimar M.F. de Carvalho

    2008-06-01

    Full Text Available OBJECTIVE: To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. METHOD: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication architecture and an artificial neural network with backpropagation learning algorithm (ANNB. RESULTS: The study was composed by 244 patients with a bigger frequency of the feminine sex. The number of right decisions at the test phase, obtained by the NEFCLASS and ANNB was 83.60% and 90.16%, respectively. The best sensibility result was attained by NEFCLASS (84.90%; the best specificity result were attained by ANNB with 95.65%. CONCLUSION: The proposed neuro-fuzzy system combined the artificial neural network capabilities in the pattern classifications together with the fuzzy logic qualitative approach, leading to a bigger rate of system success.OBJETIVO: Investigar diferentes operações aritméticas difusas para auxíliar no diagnóstico de eventos epilépticos e eventos não-epilépticos. MÉTODO: Um sistema neuro-difuso foi desenvolvido utilizando a arquitetura NEFCLASS (NEuro Fuzzy CLASSIfication e uma rede neural artificial com o algoritmo de aprendizagem backpropagation (RNAB. RESULTADOS: A amostra estudada foi de 244 pacientes com maior freqüência no sexo feminino. O número de decisões corretas na fase de teste, obtidas através do NEFCLASS e RNAB foi de 83,60% e 90,16%, respectivamente. O melhor resultado de sensibilidade foi obtido com o NEFCLASS (84,90%; o melhor resultado de especificidade foi obtido com a RNAB (95,65%. CONCLUSÃO: O sistema neuro-difuso proposto combinou a capacidade das redes neurais artificiais na classificação de padrões juntamente com a abordagem qualitativa da logica difusa, levando a maior taxa de acertos do sistema.

  5. Adaptive neuro-fuzzy controller of switched reluctance motor

    Directory of Open Access Journals (Sweden)

    Tahour Ahmed

    2007-01-01

    Full Text Available This paper presents an application of adaptive neuro-fuzzy (ANFIS control for switched reluctance motor (SRM speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI.

  6. Inference of RMR value using fuzzy set theory and neuro-fuzzy techniques

    Energy Technology Data Exchange (ETDEWEB)

    Bae, Gyu-Jin; Cho, Mahn-Sup [Korea Institute of Construction Technology, Koyang(Korea)

    2001-12-31

    In the design of tunnel, it contains inaccuracy of data, fuzziness of evaluation, observer error and so on. The face observation during tunnel excavation, therefore, plays an important role to raise stability and to reduce supporting cost. This study is carried out to minimize the subjectiveness of observer and to exactly evaluate the natural properties of ground during the face observation. For these purpose, fuzzy set theory and neuro-fuzzy techniques in artificial intelligent techniques are applied to the inference of the RMR(Rock Mass Rating) value from the observation data. The correlation between original RMR value and inferred RMR{sub {sub F}U} and RMR{sub {sub N}F} values from fuzzy Set theory and neuro-fuzzy techniques is investigated using 46 data. The results show that good correlation between original RMR value and inferred RMR{sub {sub F}U} and RMR{sub {sub N}F} values is observed when the correlation coefficients are |R|=0.96 and |R|=0.95 respectively. >From these results, applicability of fuzzy set theory and neuro-fuzzy techniques to rock mass classification is proved to be sufficiently high enough. (author). 17 refs., 5 tabs., 9 figs.

  7. 5th International Conference on Fuzzy and Neuro Computing

    CERN Document Server

    Panigrahi, Bijaya; Das, Swagatam; Suganthan, Ponnuthurai

    2015-01-01

    This proceedings bring together contributions from researchers from academia and industry to report the latest cutting edge research made in the areas of Fuzzy Computing, Neuro Computing and hybrid Neuro-Fuzzy Computing in the paradigm of Soft Computing. The FANCCO 2015 conference explored new application areas, design novel hybrid algorithms for solving different real world application problems. After a rigorous review of the 68 submissions from all over the world, the referees panel selected 27 papers to be presented at the Conference. The accepted papers have a good, balanced mix of theory and applications. The techniques ranged from fuzzy neural networks, decision trees, spiking neural networks, self organizing feature map, support vector regression, adaptive neuro fuzzy inference system, extreme learning machine, fuzzy multi criteria decision making, machine learning, web usage mining, Takagi-Sugeno Inference system, extended Kalman filter, Goedel type logic, fuzzy formal concept analysis, biclustering e...

  8. Neuro-fuzzy Control of Integrating Processes

    Directory of Open Access Journals (Sweden)

    Anna Vasičkaninová

    2011-11-01

    Full Text Available Fuzzy technology is adaptive and easily applicable in different areas.Fuzzy logic provides powerful tools to capture the perceptionof natural phenomena. The paper deals with tuning of neuro-fuzzy controllers for integrating plant and for integrating plantswith time delay. The designed approach is verified on three examples by simulations and compared plants with classical PID control.Designed fuzzy controllers lead to better closed-loop control responses then classical PID controllers.

  9. Neuro-fuzzy controller to navigate an unmanned vehicle.

    Science.gov (United States)

    Selma, Boumediene; Chouraqui, Samira

    2013-12-01

    A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).

  10. Optimization of Neuro-Fuzzy System Using Genetic Algorithm for Chromosome Classification

    Directory of Open Access Journals (Sweden)

    M. Sarosa

    2013-09-01

    Full Text Available Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural networks, and genetic algorithms. The structure consists of a four layer feed-forward neural network that uses a GBell membership function as the output function. The proposed methodology has been applied and tested on banded chromosome classification from the Copenhagen Chromosome Database. Simulation result showed that the proposed neuro-fuzzy system optimized by genetic algorithms offers advantages in setting the parameter values, improves the recognition rate significantly and decreases the training/testing time which makes genetic neuro-fuzzy system suitable for chromosome classification.

  11. Adaptive neuro-fuzzy control of ionic polymer metal composite actuators

    International Nuclear Information System (INIS)

    Thinh, Nguyen Truong; Yang, Young-Soo; Oh, Il-Kwon

    2009-01-01

    An adaptive neuro-fuzzy controller was newly designed to overcome the degradation of the actuation performance of ionic polymer metal composite actuators that show highly nonlinear responses such as a straightening-back problem under a step excitation. An adaptive control algorithm with the merits of fuzzy logic and neural networks was applied for controlling the tip displacement of the ionic polymer metal composite actuators. The reference and actual displacements and the change of the error with the electrical inputs were recorded to generate the training data. These data were used for training the adaptive neuro-fuzzy controller to find the membership functions in the fuzzy control algorithm. Software simulation and real-time experiments were conducted by using the Simulink and dSPACE environments. Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the reliable control of the ionic polymer metal composite actuator for which the performance degrades under long-time actuation

  12. ARTIFICIAL NEURAL NETWORKS, FUZZY LOGIC AND NEURO-FUZZY SYSTEM IN THE ROLE OF SHORT TERM LOAD FORECAST

    OpenAIRE

    LUIZ SABINO RIBEIRO NETO

    1999-01-01

    Esta dissertação investiga o desempenho de técnicas de inteligência computacional na previsão de carga em curto prazo. O objetivo deste trabalho foi propor e avaliar sistemas de redes neurais, lógica nebulosa, neuro-fuzzy e híbridos para previsão de carga em curto prazo, utilizando como entradas variáveis que influenciam o comportamento da carga, tais como: temperatura, índice de conforto e perfil de consumo. Este trabalho envolve 4 etapas principais: um estudo...

  13. A Neuro-Control Design Based on Fuzzy Reinforcement Learning

    DEFF Research Database (Denmark)

    Katebi, S.D.; Blanke, M.

    This paper describes a neuro-control fuzzy critic design procedure based on reinforcement learning. An important component of the proposed intelligent control configuration is the fuzzy credit assignment unit which acts as a critic, and through fuzzy implications provides adjustment mechanisms....... The fuzzy credit assignment unit comprises a fuzzy system with the appropriate fuzzification, knowledge base and defuzzification components. When an external reinforcement signal (a failure signal) is received, sequences of control actions are evaluated and modified by the action applier unit. The desirable...... ones instruct the neuro-control unit to adjust its weights and are simultaneously stored in the memory unit during the training phase. In response to the internal reinforcement signal (set point threshold deviation), the stored information is retrieved by the action applier unit and utilized for re...

  14. A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

    Science.gov (United States)

    Porwal, A.; Carranza, J.; Hale, M.

    2004-12-01

    A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

  15. Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects

    NARCIS (Netherlands)

    van der Spek, J.H.; Velthuis, W.J.R.; Veltink, Petrus H.; de Vries, Theodorus J.A.

    1996-01-01

    The authors designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three `swing phase objectives', characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller

  16. Modeling of Activated Sludge Process Using Sequential Adaptive Neuro-fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Mahsa Vajedi

    2014-10-01

    Full Text Available In this study, an adaptive neuro-fuzzy inference system (ANFIS has been applied to model activated sludge wastewater treatment process of Mobin petrochemical company. The correlation coefficients between the input variables and the output variable were calculated to determine the input with the highest influence on the output (the quality of the outlet flow in order to compare three neuro-fuzzy structures with different number of parameters. The predictions of the neuro-fuzzy models were compared with those of multilayer artificial neural network models with similar structure. The comparison indicated that both methods resulted in flexible, robust and effective models for the activated sludge system. Moreover, the root mean square of the error for neuro-fuzzy and neural network models were 5.14 and 6.59, respectively, which means the former is the superior method.

  17. Neuro-fuzzy models for systems identification applied to the operation of nuclear power plants; Sistemas neuro-fuzzy para identificacao de sistemas aplicados a operacao de centrais nucleares

    Energy Technology Data Exchange (ETDEWEB)

    Alves, Antonio Carlos Pinto Dias

    2000-09-01

    A nuclear power plant has a myriad of complex system and sub-systems that, working cooperatively, make the control of the whole plant. Nevertheless their operation be automatic most of the time, the integral understanding of their internal- logic can be away of the comprehension of even experienced operators because of the poor interpretability those controls offer. This difficulty does not happens only in nuclear power plants but in almost every a little more complex control system. Neuro-fuzzy models have been used for the last years in a attempt of suppress these difficulties because of their ability of modelling in linguist form even a system which behavior is extremely complex. This is a very intuitive human form of interpretation and neuro-fuzzy model are gathering increasing acceptance. Unfortunately, neuro-fuzzy models can grow up to become of hard interpretation because of the complexity of the systems under modelling. In general, that growing occurs in function of redundant rules or rules that cover a very little domain of the problem. This work presents an identification method for neuro-fuzzy models that not only allows models grow in function of the existent complexity but that beforehand they try to self-adapt to avoid the inclusion of new rules. This form of construction allowed to arrive to highly interpretative neuro-fuzzy models even of very complex systems. The use of this kind of technique in modelling the control of the pressurizer of a PWR nuclear power plant allowed verify its validity and how neuro-fuzzy models so built can be useful in understanding the automatic operation of a nuclear power plant. (author)

  18. Comparison between genetic fuzzy system and neuro fuzzy system to select oil wells for hydraulic fracturing; Comparacao entre genetic fuzzy system e neuro fuzzy system para selecao de pocos de petroleo para fraturamento hidraulico

    Energy Technology Data Exchange (ETDEWEB)

    Castro, Antonio Orestes de Salvo [PETROBRAS, Rio de Janeiro, RJ (Brazil); Ferreira Filho, Virgilio Jose Martins [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

    2004-07-01

    The hydraulic fracture operation is wide used to increase the oil wells production and to reduce formation damage. Reservoir studies and engineer analysis are made to select the wells for this kind of operation. As the reservoir parameters have some diffuses characteristics, Fuzzy Inference Systems (SIF) have been tested for this selection processes in the last few years. This paper compares the performance of a neuro fuzzy system and a genetic fuzzy system used for hydraulic Fracture well selection, with knowledge acquisition from an operational data base to set the SIF membership functions. The training data and the validation data used were the same for both systems. We concluded that, in despite of the genetic fuzzy system would be a younger process, it got better results than the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy system can work with constraints, the membership functions setting kept the consistency of variables linguistic values. (author)

  19. Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration

    Science.gov (United States)

    Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza

    2003-01-01

    Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.

  20. ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR END MILLING

    Directory of Open Access Journals (Sweden)

    ANGELOS P. MARKOPOULOS

    2016-09-01

    Full Text Available Soft computing is commonly used as a modelling method in various technological areas. Methods such as Artificial Neural Networks and Fuzzy Logic have found application in manufacturing technology as well. NeuroFuzzy systems, aimed to combine the benefits of both the aforementioned Artificial Intelligence methods, are a subject of research lately as have proven to be superior compared to other methods. In this paper an adaptive neuro-fuzzy inference system for the prediction of surface roughness in end milling is presented. Spindle speed, feed rate, depth of cut and vibrations were used as independent input variables, while roughness parameter Ra as dependent output variable. Several variations are tested and the results of the optimum system are presented. Final results indicate that the proposed model can accurately predict surface roughness, even for input that was not used in training.

  1. Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Boumediene ALLAOUA

    2009-12-01

    Full Text Available This paper presents an application of Adaptive Neuro-Fuzzy Inference System (ANFIS control for DC motor speed optimized with swarm collective intelligence. First, the controller is designed according to Fuzzy rules such that the systems are fundamentally robust. Secondly, an adaptive Neuro-Fuzzy controller of the DC motor speed is then designed and simulated; the ANFIS has the advantage of expert knowledge of the Fuzzy inference system and the learning capability of neural networks. Finally, the ANFIS is optimized by Swarm Intelligence. Digital simulation results demonstrate that the deigned ANFIS-Swarm speed controller realize a good dynamic behavior of the DC motor, a perfect speed tracking with no overshoot, give better performance and high robustness than those obtained by the ANFIS alone.

  2. A Genetic Based Neuro-Fuzzy Controller System

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Recently, the mobile robots have great importance in the manufacturing processes. They are widely used for assembling processes, handling the dangerous components, moving the weighted things, etc. Designing the controller of the mobile robot is a very complex task. Many simple control systems used the neuro-fuzzy controller in the mobile robots. But, they faced with great complexity when moving in unstructured and dynamic environments. The proposed system introduces the uses of the genetic algorithm for optimizing the parameters of the neuro-fuzzy controller. So, the proposed system can improve the performance of the mobile robots. It has applied for a mobile robot used for moving the dangerous and critical materials in unstructured environment. Its results are compared with other traditional controller systems. The suggested system has proved its success for the real-time applications

  3. A novel Neuro-fuzzy classification technique for data mining

    Directory of Open Access Journals (Sweden)

    Soumadip Ghosh

    2014-11-01

    Full Text Available In our study, we proposed a novel Neuro-fuzzy classification technique for data mining. The inputs to the Neuro-fuzzy classification system were fuzzified by applying generalized bell-shaped membership function. The proposed method utilized a fuzzification matrix in which the input patterns were associated with a degree of membership to different classes. Based on the value of degree of membership a pattern would be attributed to a specific category or class. We applied our method to ten benchmark data sets from the UCI machine learning repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with two powerful supervised classification algorithms Radial Basis Function Neural Network (RBFNN and Adaptive Neuro-fuzzy Inference System (ANFIS. We assessed the performance of these classification methods in terms of different performance measures such as accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and f-measure. In every aspect the proposed method proved to be superior to RBFNN and ANFIS algorithms.

  4. Classification of EEG Signals by Radial Neuro-Fuzzy Systems

    Czech Academy of Sciences Publication Activity Database

    Coufal, David

    2006-01-01

    Roč. 5, č. 2 (2006), s. 415-423 ISSN 1109-2777 R&D Projects: GA MŠk ME 701 Institutional research plan: CEZ:AV0Z10300504 Keywords : neuro-fuzzy systems * radial fuzzy systems * data mining * hybrid systems Subject RIV: BA - General Mathematics

  5. Spacecraft attitude control using neuro-fuzzy approximation of the optimal controllers

    Science.gov (United States)

    Kim, Sung-Woo; Park, Sang-Young; Park, Chandeok

    2016-01-01

    In this study, a neuro-fuzzy controller (NFC) was developed for spacecraft attitude control to mitigate large computational load of the state-dependent Riccati equation (SDRE) controller. The NFC was developed by training a neuro-fuzzy network to approximate the SDRE controller. The stability of the NFC was numerically verified using a Lyapunov-based method, and the performance of the controller was analyzed in terms of approximation ability, steady-state error, cost, and execution time. The simulations and test results indicate that the developed NFC efficiently approximates the SDRE controller, with asymptotic stability in a bounded region of angular velocity encompassing the operational range of rapid-attitude maneuvers. In addition, it was shown that an approximated optimal feedback controller can be designed successfully through neuro-fuzzy approximation of the optimal open-loop controller.

  6. A new learning algorithm for a fully connected neuro-fuzzy inference system.

    Science.gov (United States)

    Chen, C L Philip; Wang, Jing; Wang, Chi-Hsu; Chen, Long

    2014-10-01

    A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence.

  7. New concept of direct torque neuro-fuzzy control for induction motor drives. Simulation study

    Energy Technology Data Exchange (ETDEWEB)

    Grabowski, P.Z. [Institute of Control and Industrial Electronics, Warsaw University of Technology, Warsaw (Poland)

    1997-12-31

    This paper presents a new control strategy in the discrete Direct Torque Control (DTC) based on neuro-fuzzy structure. Two schemes are proposed: neuro-fuzzy switching times calculator and neuro-fuzzy incremental controller with space vector modulator. These control strategies guarantee very good dynamic and steady-states characteristics, with very low sampling time and constant switching frequency. The proposed techniques are verified by simulation study of the whole drive system and results are compared with conventional discrete Direct Torque Control method. (orig.) 18 refs.

  8. Developed adaptive neuro-fuzzy algorithm to control air conditioning ...

    African Journals Online (AJOL)

    The paper developed artificial intelligence technique adaptive neuro-fuzzy controller for air conditioning systems at different pressures. The first order Sugeno fuzzy inference system was implemented and utilized for modeling and controller design. In addition, the estimation of the heat transfer rate and water mass flow rate ...

  9. Developed adaptive neuro-fuzzy algorithm to control air conditioning ...

    African Journals Online (AJOL)

    user

    The paper developed artificial intelligence technique adaptive neuro-fuzzy ... system is highly appreciated and essential in most of our daily life. ... It can construct an input-output mapping based on human knowledge and specific input-output data ... fuzzy controllers to produce desirable internal temperature and air quality, ...

  10. Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

    Science.gov (United States)

    Akhavan, P.; Karimi, M.; Pahlavani, P.

    2014-10-01

    Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

  11. Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    P. Akhavan

    2014-10-01

    Full Text Available Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

  12. A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance

    Directory of Open Access Journals (Sweden)

    Quang Hung Do

    2013-01-01

    Full Text Available Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions.

  13. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  14. A neuro-fuzzy inference system for sensor failure detection using wavelet denoising, PCA and SPRT

    International Nuclear Information System (INIS)

    Na, Man Gyun

    2001-01-01

    In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA(principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system. The PCA is used to reduce the dimension of an input space without losing a significant amount of information, The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors

  15. Neuro-fuzzy models for systems identification applied to the operation of nuclear power plants

    International Nuclear Information System (INIS)

    Alves, Antonio Carlos Pinto Dias

    2000-09-01

    A nuclear power plant has a myriad of complex system and sub-systems that, working cooperatively, make the control of the whole plant. Nevertheless their operation be automatic most of the time, the integral understanding of their internal- logic can be away of the comprehension of even experienced operators because of the poor interpretability those controls offer. This difficulty does not happens only in nuclear power plants but in almost every a little more complex control system. Neuro-fuzzy models have been used for the last years in a attempt of suppress these difficulties because of their ability of modelling in linguist form even a system which behavior is extremely complex. This is a very intuitive human form of interpretation and neuro-fuzzy model are gathering increasing acceptance. Unfortunately, neuro-fuzzy models can grow up to become of hard interpretation because of the complexity of the systems under modelling. In general, that growing occurs in function of redundant rules or rules that cover a very little domain of the problem. This work presents an identification method for neuro-fuzzy models that not only allows models grow in function of the existent complexity but that beforehand they try to self-adapt to avoid the inclusion of new rules. This form of construction allowed to arrive to highly interpretative neuro-fuzzy models even of very complex systems. The use of this kind of technique in modelling the control of the pressurizer of a PWR nuclear power plant allowed verify its validity and how neuro-fuzzy models so built can be useful in understanding the automatic operation of a nuclear power plant. (author)

  16. A Neuro-Fuzzy Inference System Combining Wavelet Denoising, Principal Component Analysis, and Sequential Probability Ratio Test for Sensor Monitoring

    International Nuclear Information System (INIS)

    Na, Man Gyun; Oh, Seungrohk

    2002-01-01

    A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors

  17. A TSK neuro-fuzzy approach for modeling highly dynamic systems

    NARCIS (Netherlands)

    Acampora, G.

    2011-01-01

    This paper introduces a new type of TSK-based neuro-fuzzy approach and its application to modeling highly dynamic systems. In details, our proposal performs an adaptive supervised learning on a collection of time series in order to create a so-called Timed Automata Based Fuzzy Controller, i.e. an

  18. Using neuro-fuzzy based method to develop nuclear turbine cycle model

    International Nuclear Information System (INIS)

    Chan Yeakuang; Chang Chinjang

    2009-01-01

    The purpose of this study is to describe a hybrid soft-computing modeling technique used to develop the steam turbine cycle model for nuclear power plants. The technique uses neuro-fuzzy model to predict the generator output. Firstly, the plant past three fuel cycles operating data above 95% load were collected and validated as the baseline performance data set. Then the signal errors for new operating data were detected by comparison with the baseline data set and their allowable range of variations. Finally, the most important parameters were selected as an input of the neuro-fuzzy based steam turbine cycle model. After training and testing with key parameters (i.e. throttle pressure, condenser backpressure, feedwater flow rate, and final feedwater temperature), the proposed model can be used to predict the generator output. The analysis results show this neuro-fuzzy based turbine cycle model can be used to predict the generator output with a good agreement. Moreover, the achievement of this study provides an alternative approach in thermal performance evaluation for nuclear power plants. (author)

  19. Neuro-fuzzy control of structures using acceleration feedback

    Science.gov (United States)

    Schurter, Kyle C.; Roschke, Paul N.

    2001-08-01

    This paper described a new approach for the reduction of environmentally induced vibration in constructed facilities by way of a neuro-fuzzy technique. The new control technique is presented and tested in a numerical study that involves two types of building models. The energy of each building is dissipated through magnetorheological (MR) dampers whose damping properties are continuously updated by a fuzzy controller. This semi-active control scheme relies on the development of a correlation between the accelerations of the building (controller input) and the voltage applied to the MR damper (controller output). This correlation forms the basis for the development of an intelligent neuro-fuzzy control strategy. To establish a context for assessing the effectiveness of the semi-active control scheme, responses to earthquake excitation are compared with passive strategies that have similar authority for control. According to numerical simulation, MR dampers are less effective control mechanisms than passive dampers with respect to a single degree of freedom (DOF) building model. On the other hand, MR dampers are predicted to be superior when used with multiple DOF structures for reduction of lateral acceleration.

  20. dSPACE based adaptive neuro-fuzzy controller of grid interactive inverter

    International Nuclear Information System (INIS)

    Altin, Necmi; Sefa, İbrahim

    2012-01-01

    Highlights: ► We propose a dSPACE based neuro-fuzzy controlled grid interactive inverter. ► The membership functions and rule base of fuzzy logic controller by using ANFIS. ► A LCL output filter is designed. ► A high performance controller is designed. - Abstract: In this study, design, simulation and implementation of a dSPACE based grid interactive voltage source inverter are proposed. This inverter has adaptive neuro-fuzzy controller and capable of importing electrical energy, generated from renewable energy sources such as the wind, the solar and the fuel cells to the grid. A line frequency transformer and a LCL filter are used at the output of the grid interactive inverter which is designed as current controlled to decrease the susceptibility to phase errors. Membership functions and rule base of the fuzzy logic controller, which control the inverter output current, are determined by using artificial neural networks. Both simulation and experimental results show that, the grid interactive inverter operates synchronously with the grid. The inverter output current which is imported to the grid is in sinusoidal waveform and the harmonic level of it meets the international standards (4.3 < 5.0%). In addition, simulation and experimental results of the neuro-fuzzy and the PI controlled inverter are given together and compared in detail. Simulation and experimental results show that the proposed inverter has faster response to the reference variations and lower steady state error than PI controller.

  1. A neuro-fuzzy computing technique for modeling hydrological time series

    Science.gov (United States)

    Nayak, P. C.; Sudheer, K. P.; Rangan, D. M.; Ramasastri, K. S.

    2004-05-01

    Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This approach has been tested and evaluated in the field of signal processing and related areas, but researchers have only begun evaluating the potential of this neuro-fuzzy hybrid approach in hydrologic modeling studies. This paper presents the application of an adaptive neuro fuzzy inference system (ANFIS) to hydrologic time series modeling, and is illustrated by an application to model the river flow of Baitarani River in Orissa state, India. An introduction to the ANFIS modeling approach is also presented. The advantage of the method is that it does not require the model structure to be known a priori, in contrast to most of the time series modeling techniques. The results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series. The model showed good performance in terms of various statistical indices. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc. It was observed that the ANFIS model preserves the potential of the ANN approach fully, and eases the model building process.

  2. Now comes the time to defuzzify neuro-fuzzy models

    International Nuclear Information System (INIS)

    Bersini, H.; Bontempi, G.

    1996-01-01

    Fuzzy models present a singular Janus-faced : on one hand, they are knowledge-based software environments constructed from a collection of linguistic IF-THEN rules, and on the other hand, they realize nonlinear mappings which have interesting mathematical properties like low-order interpolation and universal function approximation. Neuro-fuzzy basically provides fuzzy models with the capacity, based on the available data, to compensate for the missing human knowledge by an automatic self-tuning of the structure and the parameters. A first consequence of this hybridization between the architectural and representational aspect of fuzzy models and the learning mechanisms of neural networks has been to progressively increase and fuzzify the contrast between the two Janus faces: readability or performance

  3. Adaptive Neuro-Fuzzy Computing Technique for Determining Turbulent Flow Friction Coefficient

    Directory of Open Access Journals (Sweden)

    Mohammad Givehchi

    2013-08-01

    Full Text Available Estimation of the friction coefficient in pipes is very important in many water and wastewater engineering issues, such as distribution of velocity and shear stress, erosion, sediment transport and head loss. In analyzing these problems, knowing the friction coefficient, can obtain estimates that are more accurate. In this study in order to estimate the friction coefficient in pipes, using adaptive neuro-fuzzy inference systems (ANFIS, grid partition method was used. For training and testing of neuro-fuzzy model, the data derived from the Colebrook’s equation was used. In the neuro-fuzzy approach, pipe relative roughness and Reynolds number are considered as input variables and friction coefficient as output variable is considered. Performance of the proposed approach was evaluated by using of the data obtained from the Colebrook’s equation and based on statistical indicators such as coefficient determination (R2, root mean squared error (RMSE and mean absolute error (MAE. The results showed that the adaptive nerou-fuzzy inference system with grid partition method and gauss model as an input membership function and linear as an output function could estimate friction coefficient more accurately than other conditions. The new proposed approach in this paper has capability of application in the practical design issues and can be combined with mathematical and numerical models of sediment transfer or real-time updating of these models.

  4. Innovative neuro-fuzzy system of smart transport infrastructure for road traffic safety

    Science.gov (United States)

    Beinarovica, Anna; Gorobetz, Mikhail; Levchenkov, Anatoly

    2017-09-01

    The proposed study describes applying of neural network and fuzzy logic in transport control for safety improvement by evaluation of accidents’ risk by intelligent infrastructure devices. Risk evaluation is made by following multiple-criteria: danger, changeability and influence of changes for risk increasing. Neuro-fuzzy algorithms are described and proposed for task solution. The novelty of the proposed system is proved by deep analysis of known studies in the field. The structure of neuro-fuzzy system for risk evaluation and mathematical model is described in the paper. The simulation model of the intelligent devices for transport infrastructure is proposed to simulate different situations, assess the risks and propose the possible actions for infrastructure or vehicles to minimize the risk of possible accidents.

  5. Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network Estimador neuro-fuzzy de concentração diária de biomassa da microalga Synechococcus nidulans

    Directory of Open Access Journals (Sweden)

    Vitor Badiale Furlong

    2013-02-01

    Full Text Available In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days, number of clusters (10, 30 and 50 clusters and internal weight softening parameter (Sigma (0.30, 0.45 and 0.60. These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A and 18 (B days of culture growth. The validations demonstrated that in long-term experiments (Validation A the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B, Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.Neste trabalho, foi construído um estimador neuro-fuzzy da concentração de biomassa da microalga Synechococcus nidulans a partir de concentrações iniciais da batelada, visando possibilitar a predição da produtividade. Nove experimentos em réplica foram realizados. O crescimento foi acompanhado diariamente pela transmitância do meio e mantido até o final da fase exponencial de crescimento. O treinamento das redes ocorreu segundo delineamento experimental 3³, os fatores foram o número de dias no vetor de entrada (3, 5 e 7 dias, o número de clusters (10, 30 e 50 clusters e o valor de abrandamento do filtro interno (Sigma (0,30, 0,45 e 0,60. A variável resposta foi o somatório do erro quadrático das validações. Estas possuíam 24 (A

  6. Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool.

    Science.gov (United States)

    Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

    2007-10-01

    Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.

  7. Using neuro-fuzzy based approach for the evaluation of turbine-generator outputs

    International Nuclear Information System (INIS)

    Chan, Y. K.; Lu, C. C.; Chang, C. J.; Kao, L.; Hong, L. C.

    2010-01-01

    The objective of this study is to develop a hybrid soft-computing modeling technique used to develop the steam turbine cycle model for Chinshan Nuclear Power Station (CNPS). The technique uses neuro-fuzzy model to predict the turbine-generator output. Firstly, the station past three fuel cycles operating data above 95% load were collected and validated as the baseline performance data set. Then, the signal errors for new operating data were detected by comparison with the baseline data set and their allowable range of variations. Finally, the most important parameters were selected as an input of the neuro-fuzzy based steam turbine cycle model. After training and testing with key parameters including throttle pressure, condenser back pressure, feedwater mass flow, and final feedwater temperature, the proposed model can be applied to predict the turbine-generator output. The analysis results show this neuro-fuzzy based turbine cycle model can be used to predict the generator output with a good agreement. Moreover, the achievement of this study provides an alternative approach in thermal performance evaluation for nuclear power stations. (authors)

  8. NEURO-FUZZY MODELING APPLIED IN PROGRAM MANAGEMENT TO INCREASE LOCAL PUBLIC ADMINISTRATION PERFORMANCE

    Directory of Open Access Journals (Sweden)

    Adrian-Mihai Zaharia-Radulescu

    2016-07-01

    Full Text Available One of the challenges in local public administration is dealing with an increasing number of competing requests coming from the communities they serve. The traditional approach would be to handle each request as a standalone project and be prioritized according to benefits and budget available. More and more nowadays program management is becoming a standard approach in managing the initiatives of local public administration. Program management approach is itself an enabler for performance in public sector organizations by allowing an organization to better coordinate its efforts and resources in managing a portfolio of projects. This paper aims to present how neuro-fuzzy modeling applied in program management can help an organization to increase its performance. Neuro-fuzzy modeling would lead organizations one step further by allowing them to simulate different scenarios and manage better the risks accompanying their initiatives. The research done by the authors is theoretical and combines knowledge from different areas and a neuro-fuzzy model is proposed and discussed.

  9. AN INTELLIGENT NEURO-FUZZY TERMINAL SLIDING MODE CONTROL METHOD WITH APPLICATION TO ATOMIC FORCE MICROSCOPE

    Directory of Open Access Journals (Sweden)

    Seied Yasser Nikoo

    2016-11-01

    Full Text Available In this paper, a neuro-fuzzy fast terminal sliding mode control method is proposed for controlling a class of nonlinear systems with bounded uncertainties and disturbances. In this method, a nonlinear terminal sliding surface is firstly designed. Then, this sliding surface is considered as input for an adaptive neuro-fuzzy inference system which is the main controller. A proportinal-integral-derivative controller is also used to asist the neuro-fuzzy controller in order to improve the performance of the system at the begining stage of control operation. In addition, bee algorithm is used in this paper to update the weights of neuro-fuzzy system as well as the parameters of the proportinal-integral-derivative controller. The proposed control scheme is simulated for vibration control in a model of atomic force microscope system and the results are compared with conventional sliding mode controllers. The simulation results show that the chattering effect in the proposed controller is decreased in comparison with the sliding mode and the terminal sliding mode controllers. Also, the method provides the advantages of fast convergence and low model dependency compared to the conventional methods.

  10. Global cross-station assessment of neuro-fuzzy models for estimating daily reference evapotranspiration

    Science.gov (United States)

    Shiri, Jalal; Nazemi, Amir Hossein; Sadraddini, Ali Ashraf; Landeras, Gorka; Kisi, Ozgur; Fard, Ahmad Fakheri; Marti, Pau

    2013-02-01

    SummaryAccurate estimation of reference evapotranspiration is important for irrigation scheduling, water resources management and planning and other agricultural water management issues. In the present paper, the capabilities of generalized neuro-fuzzy models were evaluated for estimating reference evapotranspiration using two separate sets of weather data from humid and non-humid regions of Spain and Iran. In this way, the data from some weather stations in the Basque Country and Valencia region (Spain) were used for training the neuro-fuzzy models [in humid and non-humid regions, respectively] and subsequently, the data from these regions were pooled to evaluate the generalization capability of a general neuro-fuzzy model in humid and non-humid regions. The developed models were tested in stations of Iran, located in humid and non-humid regions. The obtained results showed the capabilities of generalized neuro-fuzzy model in estimating reference evapotranspiration in different climatic zones. Global GNF models calibrated using both non-humid and humid data were found to successfully estimate ET0 in both non-humid and humid regions of Iran (the lowest MAE values are about 0.23 mm for non-humid Iranian regions and 0.12 mm for humid regions). non-humid GNF models calibrated using non-humid data performed much better than the humid GNF models calibrated using humid data in non-humid region while the humid GNF model gave better estimates in humid region.

  11. A NEURO FUZZY MODEL FOR THE INVESTIGATION OF ...

    African Journals Online (AJOL)

    Several factors may contribute directly or indirectly to the structural failure of metallic pipes. The most important of which is corrosion. Corrosivity of pipes is not a directly measurable parameter as pipe corrosion is a very random phenomenon. The main aim of the present study is to develop a neuro-fuzzy model capable of ...

  12. Neuro-fuzzy model for evaluating the performance of processes ...

    Indian Academy of Sciences (India)

    CHIDOZIE CHUKWUEMEKA NWOBI-OKOYE

    2017-11-16

    Nov 16, 2017 ... In this work an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to model the periodic performance of ... Since the .... The investigation hubs are a local brewing company ..... Industrial Engineers, Systems Engineers, Operations ... responsibility the overall management of the new system lies.

  13. Evaluating Loans Using a Combination of Data Envelopment and Neuro-Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Rashmi Malhotra

    2015-02-01

    Full Text Available A business organization's objective is to make better decisions at all levels of the firm to improve performance. Typically organizations are multi-faceted and complex systems that use uncertain information. Therefore, making quality decisions to improve organizational performance is a daunting task. Organizations use decision support systems that apply different business intelligence techniques such as statistical models, scoring models, neural networks, expert systems, neuro-fuzzy systems, case-based systems, or simply rules that have been developed through experience. Managers need a decision-making approach that is robust, competent, effective, efficient, and integrative to handle the multi-dimensional organizational entities. The decision maker deals with multiple players in an organization such as products, customers, competitors, location, geographic structure, scope, internal organization, and cultural dimension [46]. Sound decisions include two important concepts: efficiency (return on invested resources and effectiveness (reaching predetermined goals. However, quite frequently, the decision maker cannot simultaneously handle data from different sources. Hence, we recommend that managers analyze different aspects of data from multiple sources separately and integrate the results of the analysis. This study proposes the design of a multi-attribute-decision-support-system that combines the analytical power of two different tools: data envelopment analysis (DEA and fuzzy logic. DEA evaluates and measures the relative efficiency of decision making units that use multiple inputs and outputs to provide non-objective measures without making any specific assumptions about data. On the other hand fuzzy logic's main strength lies in handling imprecise data. This study proposes a modeling technique that jointly uses the two techniques to benefit from the two methodologies. A major advantage of the DEA approach is that it clearly identifies the

  14. UAV Controller Based on Adaptive Neuro-Fuzzy Inference System and PID

    Directory of Open Access Journals (Sweden)

    Ali Moltajaei Farid

    2013-01-01

    Full Text Available ANFIS is combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system, capable of reasoning and learning in an uncertain and imprecise environment. In this paper, an adaptive neuro-fuzzy inference system (ANFIS is employed to control an unmanned aircraft vehicle (UAV.  First, autopilots structure is defined, and then ANFIS controller is applied, to control UAVs lateral position. The results of ANFIS and PID lateral controllers are compared, where it shows the two controllers have similar results. ANFIS controller is capable to adaptation in nonlinear conditions, while PID has to be tuned to preserves proper control in some conditions. The simulation results generated by Matlab using Aerosim Aeronautical Simulation Block Set, which provides a complete set of tools for development of six degree-of-freedom. Nonlinear Aerosonde unmanned aerial vehicle model with ANFIS controller is simulated to verify the capability of the system. Moreover, the results are validated by FlightGear flight simulator.

  15. Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.

    Science.gov (United States)

    Sinha, S K; Karray, F

    2002-01-01

    Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

  16. Short term load forecasting using neuro-fuzzy networks

    Energy Technology Data Exchange (ETDEWEB)

    Hoffman, M.; Hassan, A. [South Dakota School of Mines and Technology, Rapid City, SD (United States); Martinez, D. [Black Hills Power and Light, Rapid City, SD (United States)

    2005-07-01

    Details of a neuro-fuzzy network-based short term load forecasting system for power utilities were presented. The fuzzy logic controller was used to fuzzify inputs representing historical temperature and load curves. The fuzzified inputs were then used to develop the fuzzy rules matrix. Output membership function values were determined by evaluating the fuzzified inputs with the fuzzy rules. Output membership function values were used as inputs for the neural network portion of the system. The training process used a back propagation gradient descent algorithm to adjust the weight values of the neural network in order to reduce the error between the neural network output and the desired output. The neural network was then used to predict future load values. Sample data were taken from a local power company's daily load curve to validate the system. A 10 per cent forecast error was introduced in the temperature values to determine the effect on load prediction. Results of the study suggest that the combined use of fuzzy logic and neural networks provide greater accuracy than studies where either approach is used alone. 6 refs., 6 figs.

  17. Simulation of neuro-fuzzy model for optimization of combine header setting

    Directory of Open Access Journals (Sweden)

    S Zareei

    2016-09-01

    Full Text Available Introduction The noticeable proportion of producing wheat losses occur during production and consumption steps and the loss due to harvesting with combine harvester is regarded as one of the main factors. A grain combines harvester consists of different sets of equipment and one of the most important parts is the header which comprises more than 50% of the entire harvesting losses. Some researchers have presented regression equation to estimate grain loss of combine harvester. The results of their study indicated that grain moisture content, reel index, cutter bar speed, service life of cutter bar, tine spacing, tine clearance over cutter bar, stem length were the major parameters affecting the losses. On the other hand, there are several researchswhich have used the variety of artificial intelligence methods in the different aspects of combine harvester. In neuro-fuzzy control systems, membership functions and if-then rules were defined through neural networks. Sugeno- type fuzzy inference model was applied to generate fuzzy rules from a given input-output data set due to its less time-consuming and mathematically tractable defuzzification operation for sample data-based fuzzy modeling. In this study, neuro-fuzzy model was applied to develop forecasting models which can predict the combine header loss for each set of the header parameter adjustments related to site-specific information and therefore can minimize the header loss. Materials and Methods The field experiment was conducted during the harvesting season of 2011 at the research station of the Faulty of Agriculture, Shiraz University, Shiraz, Iran. The wheat field (CV. Shiraz was harvested with a Claas Lexion-510 combine harvester. The factors which were selected as main factors influenced the header performance were three levels of reel index (RI (forward speed of combine harvester divided by peripheral speed of reel (1, 1.2, 1.5, three levels of cutting height (CH(25, 30, 35 cm, three

  18. Application of fuzzyNeuro to model weather parameter variability impacts on electrical load based on long-term forecasting

    Directory of Open Access Journals (Sweden)

    Danladi Ali

    2018-03-01

    Full Text Available Long-term load forecasting provides vital information about future load and it helps the power industries to make decision regarding electrical energy generation and delivery. In this work, fuzzyneuro model is developed to forecast a year ahead load in relation to weather parameter (temperature and humidity in Mubi, Adamawa State. It is observed that: electrical load increased with increase in temperature and relative humidity does not show notable effect on electrical load. The accuracy of the prediction is obtained at 98.78% with the corresponding mean absolute percentage error (MAPE of 1.22%. This confirms that fuzzyneuro is a good tool for load forecasting. Keywords: Electrical load, Load forecasting, Fuzzy logic, Back propagation, Neuro-fuzzy, Weather parameter

  19. Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure

    Directory of Open Access Journals (Sweden)

    Ashraf Ahmed Fahmy

    2014-03-01

    Full Text Available This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the input-output space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation.  Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm.

  20. Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.

    Science.gov (United States)

    Ahmadieh, Hajar; Asl, Babak Mohammadzadeh

    2017-04-01

    We proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems. The method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body. So, the components of the MECG are estimated from the abdominal signal. By subtracting estimated mother cardiac signal from abdominal signal, fetal cardiac signal can be extracted. This algorithm was applied on synthetic ECG signals generated based on the models developed by McSharry et al. and Behar et al. and also on DaISy real database. In environments with high uncertainty, our method performs better than the Type-1 fuzzy method. Specifically, in evaluation of the algorithm with the synthetic data based on McSharry model, for input signals with SNR of -5dB, the SNR of the extracted FECG was improved by 38.38% in comparison with the Type-1 fuzzy method. Also, the results show that increasing the uncertainty or decreasing the input SNR leads to increasing the percentage of the improvement in SNR of the extracted FECG. For instance, when the SNR of the input signal decreases to -30dB, our proposed algorithm improves the SNR of the extracted FECG by 71.06% with respect to the Type-1 fuzzy method. The same results were obtained on synthetic data based on Behar model. Our results on real database reflect the success of the proposed method to separate the maternal and fetal heart signals even if their waves overlap in time. Moreover, the proposed algorithm was applied to the simulated fetal ECG with ectopic beats and achieved good results in separating FECG from MECG. The results show the superiority of the proposed Type-2 neuro-fuzzy inference method over the Type-1 neuro-fuzzy inference and the polynomial networks methods, which is due to its

  1. Recognition of Handwritten Arabic words using a neuro-fuzzy network

    International Nuclear Information System (INIS)

    Boukharouba, Abdelhak; Bennia, Abdelhak

    2008-01-01

    We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database

  2. Adaptive neuro-fuzzy optimization of wind farm project net profit

    International Nuclear Information System (INIS)

    Shamshirband, Shahaboddin; Petković, Dalibor; Ćojbašić, Žarko; Nikolić, Vlastimir; Anuar, Nor Badrul; Mohd Shuib, Nor Liyana; Mat Kiah, Miss Laiha; Akib, Shatirah

    2014-01-01

    Highlights: • Analyzing of wind farm project investment. • Net present value (NPV) maximization of the wind farm project. • Adaptive neuro-fuzzy (ANFIS) optimization of the number of wind turbines to maximize NPV. • The impact of the variation in the wind farm parameters. • Adaptive neuro fuzzy application. - Abstract: A wind power plant which consists of a group of wind turbines at a specific location is also known as wind farm. To maximize the wind farm net profit, the number of turbines installed in the wind farm should be different in depend on wind farm project investment parameters. In this paper, in order to achieve the maximal net profit of a wind farm, an intelligent optimization scheme based on the adaptive neuro-fuzzy inference system (ANFIS) is applied. As the net profit measures, net present value (NPV) and interest rate of return (IRR) are used. The NPV and IRR are two of the most important criteria for project investment estimating. The general approach in determining the accept/reject/stay in different decision for a project via NPV and IRR is to treat the cash flows as known with certainty. However, even small deviations from the predetermined values may easily invalidate the decision. In the proposed model the ANFIS estimator adjusts the number of turbines installed in the wind farm, for operating at the highest net profit point. The performance of proposed optimizer is confirmed by simulation results. Some outstanding properties of this new estimator are online implementation capability, structural simplicity and its robustness against any changes in wind farm parameters. Based on the simulation results, the effectiveness of the proposed optimization strategy is verified

  3. System identification of smart structures using a wavelet neuro-fuzzy model

    Science.gov (United States)

    Mitchell, Ryan; Kim, Yeesock; El-Korchi, Tahar

    2012-11-01

    This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure-MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input-output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.

  4. System identification of smart structures using a wavelet neuro-fuzzy model

    International Nuclear Information System (INIS)

    Mitchell, Ryan; Kim, Yeesock; El-Korchi, Tahar

    2012-01-01

    This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure–MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input–output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure–MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe. (paper)

  5. Estimating Reservoir Inflow Using RADAR Forecasted Precipitation and Adaptive Neuro Fuzzy Inference System

    Science.gov (United States)

    Yi, J.; Choi, C.

    2014-12-01

    Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.

  6. Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

    Directory of Open Access Journals (Sweden)

    Bogdan Gliwa

    2011-01-01

    Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

  7. A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system

    Energy Technology Data Exchange (ETDEWEB)

    Chaouachi, Aymen; Kamel, Rashad M.; Nagasaka, Ken [Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Nakamachi (Japan)

    2010-12-15

    This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P and O) algorithm dispositive. (author)

  8. A neuro-fuzzy model for prediction of the indoor temperature in typical Australian residential buildings

    Energy Technology Data Exchange (ETDEWEB)

    Alasha' ary, Haitham; Moghtaderi, Behdad; Page, Adrian; Sugo, Heber [Priority Research Centre for Energy, Chemical Engineering, School of Engineering, Faculty of Engineering and Built Environment, the University of Newcastle, Callaghan, Newcastle, NSW 2308 (Australia)

    2009-07-15

    The Masonry Research Group at The University of Newcastle, Australia has embarked on an extensive research program to study the thermal performance of common walling systems in Australian residential buildings by studying the thermal behaviour of four representative purpose-built thermal test buildings (referred to as 'test modules' or simply 'modules' hereafter). The modules are situated on the university campus and are constructed from brick veneer (BV), cavity brick (CB) and lightweight (LW) constructions. The program of study has both experimental and analytical strands, including the use of a neuro-fuzzy approach to predict the thermal behaviour. The latter approach employs an experimental adaptive neuro-fuzzy inference system (ANFIS) which is used in this study to predict the room (indoor) temperatures of the modules under a range of climatic conditions pertinent to Newcastle (NSW, Australia). The study shows that this neuro-fuzzy model is capable of accurately predicting the room temperature of such buildings; thus providing a potential computationally efficient and inexpensive predictive tool for the more effective thermal design of housing. (author)

  9. Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

    Science.gov (United States)

    Ibrahim, Wael Refaat Anis

    The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an

  10. Prediction of flood abnormalities for improved public safety using a modified adaptive neuro-fuzzy inference system.

    Science.gov (United States)

    Aqil, M; Kita, I; Yano, A; Nishiyama, S

    2006-01-01

    It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other

  11. Forecasting Monthly Electricity Demands by Wavelet Neuro-Fuzzy System Optimized by Heuristic Algorithms

    Directory of Open Access Journals (Sweden)

    Jeng-Fung Chen

    2018-02-01

    Full Text Available Electricity load forecasting plays a paramount role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate planning and prediction of electricity load are therefore vital. In this study, a novel approach for forecasting monthly electricity demands by wavelet transform and a neuro-fuzzy system is proposed. Firstly, the most appropriate inputs are selected and a dataset is constructed. Then, Haar wavelet transform is utilized to decompose the load data and eliminate noise. In the model, a hierarchical adaptive neuro-fuzzy inference system (HANFIS is suggested to solve the curse-of-dimensionality problem. Several heuristic algorithms including Gravitational Search Algorithm (GSA, Cuckoo Optimization Algorithm (COA, and Cuckoo Search (CS are utilized to optimize the clustering parameters which help form the rule base, and adaptive neuro-fuzzy inference system (ANFIS optimize the parameters in the antecedent and consequent parts of each sub-model. The proposed approach was applied to forecast the electricity load of Hanoi, Vietnam. The constructed models have shown high forecasting performances based on the performance indices calculated. The results demonstrate the validity of the approach. The obtained results were also compared with those of several other well-known methods including autoregressive integrated moving average (ARIMA and multiple linear regression (MLR. In our study, the wavelet CS-HANFIS model outperformed the others and provided more accurate forecasting.

  12. A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers

    International Nuclear Information System (INIS)

    Nguyen, Sy Dzung; Choi, Seung-Bok

    2012-01-01

    This paper proposes a new algorithm, named establishing neuro-fuzzy system (ENFS), to identify dynamic characteristics of smart dampers such as magnetorheological (MR) and electrorheological (ER) dampers. In the ENFS, data clustering is performed based on the proposed algorithm named partitioning data space (PDS). Firstly, the PDS builds data clusters in joint input–output data space with appropriate constraints. The role of these constraints is to create reasonable data distribution in clusters. The ENFS then uses these clusters to perform the following tasks. Firstly, the fuzzy sets expressing characteristics of data clusters are established. The structure of the fuzzy sets is adjusted to be suitable for features of the data set. Secondly, an appropriate structure of neuro-fuzzy (NF) expressed by an optimal number of labeled data clusters and the fuzzy-set groups is determined. After the ENFS is introduced, its effectiveness is evaluated by a prediction-error-comparative work between the proposed method and some other methods in identifying numerical data sets such as ‘daily data of stock A’, or in identifying a function. The ENFS is then applied to identify damping force characteristics of the smart dampers. In order to evaluate the effectiveness of the ENFS in identifying the damping forces of the smart dampers, the prediction errors are presented by comparing with experimental results. (paper)

  13. A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers

    Science.gov (United States)

    Dzung Nguyen, Sy; Choi, Seung-Bok

    2012-08-01

    This paper proposes a new algorithm, named establishing neuro-fuzzy system (ENFS), to identify dynamic characteristics of smart dampers such as magnetorheological (MR) and electrorheological (ER) dampers. In the ENFS, data clustering is performed based on the proposed algorithm named partitioning data space (PDS). Firstly, the PDS builds data clusters in joint input-output data space with appropriate constraints. The role of these constraints is to create reasonable data distribution in clusters. The ENFS then uses these clusters to perform the following tasks. Firstly, the fuzzy sets expressing characteristics of data clusters are established. The structure of the fuzzy sets is adjusted to be suitable for features of the data set. Secondly, an appropriate structure of neuro-fuzzy (NF) expressed by an optimal number of labeled data clusters and the fuzzy-set groups is determined. After the ENFS is introduced, its effectiveness is evaluated by a prediction-error-comparative work between the proposed method and some other methods in identifying numerical data sets such as ‘daily data of stock A’, or in identifying a function. The ENFS is then applied to identify damping force characteristics of the smart dampers. In order to evaluate the effectiveness of the ENFS in identifying the damping forces of the smart dampers, the prediction errors are presented by comparing with experimental results.

  14. Experimental investigation of the direct torque neuro-fuzzy controller for induction motor drive

    Energy Technology Data Exchange (ETDEWEB)

    Grabowski, P.Z.; Kazmierkowski, M.P. [Warsaw Univ. of Technology (Poland)

    2000-08-01

    In this paper, the concept and implementation of a new simple Direct Torque Neuro-Fuzzy Control (DTNFC) scheme for PWM inverter-fed induction motor drive are presented. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to achieve high performance decoupled flux and torque control. The theoretical principle and tuning procedure of this method are discussed. A 3 kW induction motor experimental system with digital signal processor (DSP type) TMS 320C31 based controller has been built to verify this approach. The simulation and laboratory experimental results, which illustrate the performance of the proposed scheme, are presented. Also, nomograms for controller design are given. It has been shown that the simple DTNFC is characterised by very fast torque and flux response, very low speed operation and simple tuning capability. (orig.)

  15. Integration of Fault Detection and Isolation with Control Using Neuro-fuzzy Scheme

    Directory of Open Access Journals (Sweden)

    A. Asokan

    2009-10-01

    Full Text Available In this paper an algorithms is developed for fault diagnosis and fault tolerant control strategy for nonlinear systems subjected to an unknown time-varying fault. At first, the design of fault diagnosis scheme is performed using model based fault detection technique. The neuro-fuzzy chi-square scheme is applied for fault detection and isolation. The fault magnitude and time of occurrence of fault is obtained through neuro-fuzzy chi-square scheme. The estimated magnitude of the fault magnitude is normalized and used by the feed-forward control algorithm to make appropriate changes in the manipulated variable to keep the controlled variable near its set value. The feed-forward controller acts along with feed-back controller to control the multivariable system. The performance of the proposed scheme is applied to a three- tank process for various types of fault inputs to show the effectiveness of the proposed approach.

  16. A comparative study of ANN and neuro-fuzzy for the prediction of ...

    Indian Academy of Sciences (India)

    Istanbul Technical University, Faculty of Civil Engineering, Hydraulics and Water. Resources Division, Maslak 34469, Istanbul, Turkey. Singh et al (2005) examined the potential of the ANN and neuro-fuzzy systems application for the prediction of dynamic constant of rockmass. However, the model proposed by them has ...

  17. Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network

    Science.gov (United States)

    Huang, Yuan-Ruey; Kang, Yuan; Chu, Ming-Hui; Chang, Yeon-Pun

    A novel Chebyshev functional recurrent neuro-fuzzy (CFRNF) network is developed from a combination of the Takagi-Sugeno-Kang (TSK) fuzzy model and the Chebyshev recurrent neural network (CRNN). The CFRNF network can emulate the nonlinear dynamics of a servomechanism system. The system nonlinearity is addressed by enhancing the input dimensions of the consequent parts in the fuzzy rules due to functional expansion of a Chebyshev polynomial. The back propagation algorithm is used to adjust the parameters of the antecedent membership functions as well as those of consequent functions. To verify the performance of the proposed CFRNF, the experiment of the belt servomechanism is presented in this paper. Both of identification methods of adaptive neural fuzzy inference system (ANFIS) and recurrent neural network (RNN) are also studied for modeling of the belt servomechanism. The analysis and comparison results indicate that CFRNF makes identification of complex nonlinear dynamic systems easier. It is verified that the accuracy and convergence of the CFRNF are superior to those of ANFIS and RNN by the identification results of a belt servomechanism.

  18. Diagnosis Penyakit Jantung Menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS

    Directory of Open Access Journals (Sweden)

    Khadijah Fahmi Hayati Holle

    2016-09-01

    Full Text Available The number of uncertain risk factor in heart disease makes experts difficult to diagnose its disease. Computer technology in the health field is mostly used. In this paper, we implement a system to diagnose heart disease. The used method is Adaptive neuro-fuzzy inference system which combine the advantage of fuzzy and neural network. The used data is UCI Cleveland data that have 13 attributes as inputs. Output system diagnosis compared with observational data for evaluation. System performance tested by calculating accuracy. Tests were also conducted on the variation of the learning rate, iteration, minimum error, and the use of membership functions. Accuracy obtained from test is 65,657% where using membership function Beta.

  19. Design and implementation of an adaptive critic-based neuro-fuzzy controller on an unmanned bicycle

    OpenAIRE

    Shafiekhani, Ali; Mahjoob, Mohammad J.; Akraminia, Mehdi

    2017-01-01

    Fuzzy critic-based learning forms a reinforcement learning method based on dynamic programming. In this paper, an adaptive critic-based neuro-fuzzy system is presented for an unmanned bicycle. The only information available for the critic agent is the system feedback which is interpreted as the last action performed by the controller in the previous state. The signal produced by the critic agent is used along with the error back propagation to tune (online) conclusion parts of the fuzzy infer...

  20. Determination of the Main Influencing Factors on Road Fatalities Using an Integrated Neuro-Fuzzy Algorithm

    Directory of Open Access Journals (Sweden)

    Amir Masoud Rahimi

    Full Text Available Abstract This paper proposed an integrated algorithm of neuro-fuzzy techniques to examine the complex impact of socio-technical influencing factors on road fatalities. The proposed algorithm could handle complexity, non-linearity and fuzziness in the modeling environment due to its mechanism. The Neuro-fuzzy algorithm for determination of the potential influencing factors on road fatalities consisted of two phases. In the first phase, intelligent techniques are compared for their improved accuracy in predicting fatality rate with respect to some socio-technical influencing factors. Then in the second phase, sensitivity analysis is performed to calculate the pure effect on fatality rate of the potential influencing factors. The applicability and usefulness of the proposed algorithm is illustrated using the data in Iran provincial road transportation systems in the time period 2012-2014. Results show that road design improvement, number of trips, and number of passengers are the most influencing factors on provincial road fatality rate.

  1. Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications

    Directory of Open Access Journals (Sweden)

    A.A. Fahmy

    2013-12-01

    Full Text Available This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required inverse modeling rules from input/output data recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the online parameter learning phase. Finally, a fuzzy-PID-like incremental controller was employed as Feedback servo controller. The proposed control system was tested using dynamic model of a six-axis industrial robot. The control system showed good results compared to the conventional PID individual joint controller.

  2. Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Syed Zulqadar Hassan

    2017-03-01

    Full Text Available An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

  3. Landslide susceptibility mapping using a neuro-fuzzy

    Science.gov (United States)

    Lee, S.; Choi, J.; Oh, H.

    2009-12-01

    This paper develops and applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. A neuro-fuzzy system is based on a fuzzy system that is trained by a learning algorithm derived from the neural network theory. The learning procedure operates on local information, and causes only local modifications in the underlying fuzzy system. The study area, Boun, suffered much damage following heavy rain in 1998 and was selected as a suitable site for the evaluation of the frequency and distribution of landslides. Boun is located in the central part of Korea. Landslide-related factors such as slope, soil texture, wood type, lithology, and density of lineament were extracted from topographic, soil, forest, and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs) and analysis results were verified using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping. Various MFs were used in this study, and after verification, the difference in accuracy according to the MFs was small, between 84.81% and 84.96%. The difference was just 0.15% and therefore the choice of MFs was not important in the study. Also, compared with the likelihood ratio model, which showed 84.94%, the accuracy was similar. Thus, the ANFIS could be applied to other study areas with different data and other study methods such as cross-validation. The developed ANFIS learns the if-then rules between landslide-related factors and landslide

  4. Design of uav robust autopilot based on adaptive neuro-fuzzy inference system

    Directory of Open Access Journals (Sweden)

    Mohand Achour Touat

    2008-04-01

    Full Text Available  This paper is devoted to the application of adaptive neuro-fuzzy inference systems to the robust control of the UAV longitudinal motion. The adaptive neore-fuzzy inference system model needs to be trained by input/output data. This data were obtained from the modeling of a ”crisp” robust control system. The synthesis of this system is based on the separation theorem, which defines the structure and parameters of LQG-optimal controller, and further - robust optimization of this controller, based on the genetic algorithm. Such design procedure can define the rule base and parameters of fuzzyfication and defuzzyfication algorithms of the adaptive neore-fuzzy inference system controller, which ensure the robust properties of the control system. Simulation of the closed loop control system of UAV longitudinal motion with adaptive neore-fuzzy inference system controller demonstrates high efficiency of proposed design procedure.

  5. Design and simplification of Adaptive Neuro-Fuzzy Inference Controllers for power plants

    Energy Technology Data Exchange (ETDEWEB)

    Alturki, F.A.; Abdennour, A. [King Saud University, Riyadh (Saudi Arabia). Electrical Engineering Dept.

    1999-10-01

    This article presents the design of an Adaptive Neuro-Fuzzy Inference Controller (ANFIC) for a 160 MW power plant. The space of operating conditions of the plant is partitioned into five regions. For each of the regions, an optimal controller is designed to meet a set of design objectives. The resulting five linear controllers are used to train the ANFIC. To enhance the applicability of the control system, a new algorithm that reduces the fuzzy rules to the most essential ones is also presented. This algorithm offers substantial savings in computation time while maintaining the performance and robustness of the original controller. (author)

  6. Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

    1997-12-01

    This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.

  7. NEURO-FUZZY MODELLING OF BLENDING PROCESS IN CEMENT PLANT

    Directory of Open Access Journals (Sweden)

    Dauda Olarotimi Araromi

    2015-11-01

    Full Text Available The profitability of a cement plant depends largely on the efficient operation of the blending stage, therefore, there is a need to control the process at the blending stage in order to maintain the chemical composition of the raw mix near or at the desired value with minimum variance despite variation in the raw material composition. In this work, neuro-fuzzy model is developed for a dynamic behaviour of the system to predict the total carbonate content in the raw mix at different clay feed rates. The data used for parameter estimation and model validation was obtained from one of the cement plants in Nigeria. The data was pre-processed to remove outliers and filtered using smoothening technique in order to reveal its dynamic nature. Autoregressive exogenous (ARX model was developed for comparison purpose. ARX model gave high root mean square error (RMSE of 5.408 and 4.0199 for training and validation respectively. Poor fit resulting from ARX model is an indication of nonlinear nature of the process. However, both visual and statistical analyses on neuro-fuzzy (ANFIS model gave a far better result. RMSE of training and validation are 0.28167 and 0.7436 respectively, and the sum of square error (SSE and R-square are 39.6692 and 0.9969 respectively. All these are an indication of good performance of ANFIS model. This model can be used for control design of the process.

  8. Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model.

    Directory of Open Access Journals (Sweden)

    Georgina Cosma

    Full Text Available The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA level, the biopsy most common tumor pattern (Primary Gleason pattern and the second most common tumor pattern (Secondary Gleason pattern in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD or Extra-Prostatic Disease (ED using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC, with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812. The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR

  9. Textual and shape-based feature extraction and neuro-fuzzy classifier for nuclear track recognition

    Science.gov (United States)

    Khayat, Omid; Afarideh, Hossein

    2013-04-01

    Track counting algorithms as one of the fundamental principles of nuclear science have been emphasized in the recent years. Accurate measurement of nuclear tracks on solid-state nuclear track detectors is the aim of track counting systems. Commonly track counting systems comprise a hardware system for the task of imaging and software for analysing the track images. In this paper, a track recognition algorithm based on 12 defined textual and shape-based features and a neuro-fuzzy classifier is proposed. Features are defined so as to discern the tracks from the background and small objects. Then, according to the defined features, tracks are detected using a trained neuro-fuzzy system. Features and the classifier are finally validated via 100 Alpha track images and 40 training samples. It is shown that principle textual and shape-based features concomitantly yield a high rate of track detection compared with the single-feature based methods.

  10. Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

    Science.gov (United States)

    Sdao, F.; Lioi, D. S.; Pascale, S.; Caniani, D.; Mancini, I. M.

    2013-02-01

    The complete assessment of landslide susceptibility needs uniformly distributed detailed information on the territory. This information, which is related to the temporal occurrence of landslide phenomena and their causes, is often fragmented and heterogeneous. The present study evaluates the landslide susceptibility map of the Natural Archaeological Park of Matera (Southern Italy) (Sassi and area Rupestrian Churches sites). The assessment of the degree of "spatial hazard" or "susceptibility" was carried out by the spatial prediction regardless of the return time of the events. The evaluation model for the susceptibility presented in this paper is very focused on the use of innovative techniques of artificial intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network. The method described in this paper is a novel technique based on a neuro-fuzzy system. It is able to train data like neural network and it is able to shape and control uncertain and complex systems like a fuzzy system. This methodology allows us to derive susceptibility maps of the study area. These data are obtained from thematic maps representing the parameters responsible for the instability of the slopes. The parameters used in the analysis are: plan curvature, elevation (DEM), angle and aspect of the slope, lithology, fracture density, kinematic hazard index of planar and wedge sliding and toppling. Moreover, this method is characterized by the network training which uses a training matrix, consisting of input and output training data, which determine the landslide susceptibility. The neuro-fuzzy method was integrated to a sensitivity analysis in order to overcome the uncertainty linked to the used membership functions. The method was compared to the landslide inventory map and was validated by applying three methods: a ROC (Receiver Operating Characteristic) analysis, a confusion matrix and a SCAI method. The developed neuro-fuzzy method showed a good performance in the

  11. Identifikasi Gangguan Neurologis Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS

    Directory of Open Access Journals (Sweden)

    Jani Kusanti

    2015-07-01

    Abstract             The use of Adaptive Neuro Fuzzy Inference System (ANFIS methods in the process of identifying one of neurological disorders in the head, known in medical terms ischemic stroke from the ct scan of the head in order to identify the location of ischemic stroke. The steps are performed in the extraction process of identifying, among others, the image of the ct scan of the head by using a histogram. Enhanced image of the intensity histogram image results using Otsu threshold to obtain results pixels rated 1 related to the object while pixel rated 0 associated with the measurement background. The result used for image clustering process, to process image clusters used fuzzy c-mean (FCM clustering result is a row of the cluster center, the results of the data used to construct a fuzzy inference system (FIS. Fuzzy inference system applied is fuzzy inference model of Takagi-Sugeno-Kang. In this study ANFIS is used to optimize the results of the determination of the location of the blockage ischemic stroke. Used recursive least squares estimator (RLSE for learning. RMSE results obtained in the training process of 0.0432053, while in the process of generated test accuracy rate of 98.66%   Keywords— Stroke Ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE

  12. Characterization and modeling of a new magnetorheological damper with meandering type valve using neuro-fuzzy

    Directory of Open Access Journals (Sweden)

    Fitrian Imaduddin

    2017-10-01

    Full Text Available This paper presents the characterization and hysteresis modeling of magnetorheological (MR damper with meandering type valve. The meandering type MR valve, which employs the combination of multiple annular and radial flow passages, has been introduced as the new type of high performance MR valve with higher achievable pressure drop and controllable performance range than similar counterparts in its class. Since the performance of a damper is highly determined by the valve performance, the utilization of the meandering type MR valve in an MR damper could potentially improve the damper performance. The damping force characterization of the MR damper is conducted by measuring the damping force as a response to the variety of harmonic excitations. The hysteresis behavior of the damper is identified by plotting the damping force relationship to the excitation displacement and velocity. For the hysteresis modeling purpose, some parts of the data are taken as the training data source for the optimization parameters in the neuro-fuzzy model. The performance of the trained neuro-fuzzy model is assessed by validating the model output with the remaining measurement data and benchmarking the results with the output of the parametric hysteresis model. The validation results show that the neuro-fuzzy model is demonstrating good agreement with the measurement results indicated by the average relative error of only around 7%. The model also shows robustness with no tendency of growing error when the input values are changed.

  13. Estudo da logística de distribuição física de um laticínio utilizando lógica fuzzy Study of physical distribution logistics of a dairy industry using fuzzy logic

    Directory of Open Access Journals (Sweden)

    Alysson Vinícius Neves dos Santos

    2012-08-01

    Full Text Available Este estudo versa sobre a viabilidade da aplicação da lógica fuzzy para auxiliar o gerente de uma indústria de laticínios na tomada de decisão na área de logística de distribuição física. O estudo de caso realizado numa indústria de laticínios de pequeno porte em Minas Gerais identificou que, a cada novo pedido solicitado via internet, o profissional de logística manipula ao mesmo tempo um grande número de informações e decide, com base na sua experiência, a melhor forma de distribuição desses pedidos. A proposta deste trabalho é apresentar um modelo fuzzy como uma alternativa de apoio aos métodos tradicionais de processamento de pedidos e tomada de decisão logística. Os resultados permitiram concluir que o modelo foi transcrito de forma adequada ao problema proposto ao apresentar as mesmas decisões tomadas pelo especialista e que, se observados os pontos fracos detectados, este modelo pode se constituir em importante ferramenta para a competitividade das indústrias.This study focuses on the viability of fuzzy logic application to assist the management of a dairy industry in the process of decision-making in physical distribution logistics. The case study, carried out in a small dairy industry in the State of Minas Gerais, identified that, for each new order request via internet, the logistician copes with a large amount of information at the same time and decides, based on experience, the best way to distribute such orders. The purpose of this research was to present a fuzzy model as an alternative support to traditional methods for order processing and logistics decision-making. The results showed that the model adequately reproduced the problem proposed as it produced the same decisions made by the specialist and, if the weak points detected are observed, this model can be used as an important tool for the competitiveness of industries.

  14. Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

    Directory of Open Access Journals (Sweden)

    Vitor Badiale Furlong

    2013-02-01

    Full Text Available In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days, number of clusters (10, 30 and 50 clusters and internal weight softening parameter (Sigma (0.30, 0.45 and 0.60. These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A and 18 (B days of culture growth. The validations demonstrated that in long-term experiments (Validation A the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B, Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.

  15. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

    Science.gov (United States)

    Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

    2012-06-01

    SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

  16. Application of neuro-fuzzy model for neutron activation analysis (NAA)

    International Nuclear Information System (INIS)

    Khalafi, H.; Terman, M.S.; Rahmani, F.

    2011-01-01

    Neutron activation analysis (NAA) is a precise chemical multielemental method of analysis which is satisfactorily used for qualitative and quantitative analyses. Repeated irradiation is needed because of mal-determination of some elements due to peak overlap in qualitative analysis. In this study, NAA procedure has been modified using a neuro-fuzzy model to avoid repeated irradiation based on multilayer perceptrons network trained by the Levenberg Marquardt algorithm. This method increases the precision of spectrum analysis in the case of strong background and peak overlap. (authors)

  17. Evaluating Loans Using a Combination of Data Envelopment and Neuro-Fuzzy Systems

    OpenAIRE

    Rashmi Malhotra; D.K. Malhotra

    2015-01-01

    A business organization's objective is to make better decisions at all levels of the firm to improve performance. Typically organizations are multi-faceted and complex systems that use uncertain information. Therefore, making quality decisions to improve organizational performance is a daunting task. Organizations use decision support systems that apply different business intelligence techniques such as statistical models, scoring models, neural networks, expert systems, neuro-fuzzy systems, ...

  18. A neuro-fuzzy controller for xenon spatial oscillations in load-following operation

    Energy Technology Data Exchange (ETDEWEB)

    Na, Man Gyun [Chosun University, Kwangju (Korea, Republic of); Upadhyaya, Belle R [The University of Tennessee, Knoxville (United States)

    1998-12-31

    A neuro-fuzzy control algorithm is applied for xenon spatial oscillations in a pressurized water reactor. The consequent and antecedent parameters of the fuzzy rules are tuned by the gradient descent method. The reactor model used for computer simulations is a two-point xenon oscillation model. The reactor core is axially divided into two regions and each region has one input and one output and is coupled with the other region. The interaction between the regions of the reactor core is treated by a decoupling scheme. This proposed control method exhibits very responses to a step or a ramp change of target axial offest without any residual flux oscillations. 9 refs., 5 figs. (Author)

  19. A neuro-fuzzy controller for xenon spatial oscillations in load-following operation

    Energy Technology Data Exchange (ETDEWEB)

    Na, Man Gyun [Chosun University, Kwangju (Korea, Republic of); Upadhyaya, Belle R. [The University of Tennessee, Knoxville (United States)

    1997-12-31

    A neuro-fuzzy control algorithm is applied for xenon spatial oscillations in a pressurized water reactor. The consequent and antecedent parameters of the fuzzy rules are tuned by the gradient descent method. The reactor model used for computer simulations is a two-point xenon oscillation model. The reactor core is axially divided into two regions and each region has one input and one output and is coupled with the other region. The interaction between the regions of the reactor core is treated by a decoupling scheme. This proposed control method exhibits very responses to a step or a ramp change of target axial offest without any residual flux oscillations. 9 refs., 5 figs. (Author)

  20. An efficient Neuro-Fuzzy approach to nuclear power plant transient identification

    Energy Technology Data Exchange (ETDEWEB)

    Gomes da Costa, Rafael [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Abreu Mol, Antonio Carlos de, E-mail: mol@ien.gov.br [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Instituto Nacional de C and T de Reatores Nucleares Inovadores (Brazil); Carvalho, Paulo Victor R. de, E-mail: paulov@ien.gov.br [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Lapa, Celso Marcelo Franklin, E-mail: lapa@ien.gov.br [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Instituto Nacional de C and T de Reatores Nucleares Inovadores (Brazil)

    2011-06-15

    Highlights: > We investigate a Neuro-Fuzzy modeling tool use for able transient identification. > The prelusive transient type identification is done by an artificial neural network. > After, the fuzzy-logic system analyzes the results emitting reliability degree of it. > The research support was made in a PWR simulator at the Brazilian Nuclear Engineering Institute. > The results show the potential to help operators' decisions in a nuclear power plant. - Abstract: Transient identification in nuclear power plants (NPP) is often a computational very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Recently, several works have been developed for transient identification. These works frequently present a non reliable response, using the 'don't know' as the system output. In this work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A validation of this identification system was made at the three loops Pressurized Water Reactor (PWR) simulator of the Human-System Interface Laboratory (LABIHS) of the Nuclear Engineering Institute

  1. An efficient Neuro-Fuzzy approach to nuclear power plant transient identification

    International Nuclear Information System (INIS)

    Gomes da Costa, Rafael; Abreu Mol, Antonio Carlos de; Carvalho, Paulo Victor R. de; Lapa, Celso Marcelo Franklin

    2011-01-01

    Highlights: → We investigate a Neuro-Fuzzy modeling tool use for able transient identification. → The prelusive transient type identification is done by an artificial neural network. → After, the fuzzy-logic system analyzes the results emitting reliability degree of it. → The research support was made in a PWR simulator at the Brazilian Nuclear Engineering Institute. → The results show the potential to help operators' decisions in a nuclear power plant. - Abstract: Transient identification in nuclear power plants (NPP) is often a computational very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Recently, several works have been developed for transient identification. These works frequently present a non reliable response, using the 'don't know' as the system output. In this work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A validation of this identification system was made at the three loops Pressurized Water Reactor (PWR) simulator of the Human-System Interface Laboratory (LABIHS) of the Nuclear Engineering Institute (IEN

  2. UM MODELO INFORMACIONAL PARA EMPRESAS MULTIPLANTA

    Directory of Open Access Journals (Sweden)

    Rogério Matos Dias

    2006-09-01

    Full Text Available Este estudo objetiva conceber, testar e demonstrar a aplicabilidade de um modelo informacional que possibilite a gestão integrada das operações da cadeia de suprimentos para firmas geograficamente dispersas – também denominadas empresas multiplanta. Para tanto, foram aplicados os métodos de pesquisa de caso e pesquisa-ação em uma empresa representativa do setor mínero-metalúrgico em âmbito mundial, a Rio Doce Manganês S.A., subsidiária da Cia. Vale do Rio Doce. A pesquisa pretende demonstrar que o modelo de sistema informacional proposto, por meio da automatização dos processos transacionais e gerenciais, é capaz de prover recursos de planejamento e controle nos níveis operacional, tático e estratégico, devido a sua capacidade de incrementar velocidade e qualidade nos processos de análise e decisão. Adicionalmente, pretende conduzir, após análise dos resultados e avaliação das contribuições a empresa estudada, a um modelo informacional adequado para gerir as operações de outras empresas de mineração e metalurgia com características semelhantes.

  3. Ensemble empirical model decomposition and neuro-fuzzy conjunction model for middle and long-term runoff forecast

    Science.gov (United States)

    Tan, Q.

    2017-12-01

    Forecasting the runoff over longer periods, such as months and years, is one of the important tasks for hydrologists and water resource managers to maximize the potential of the limited water. However, due to the nonlinear and nonstationary characteristic of the natural runoff, it is hard to forecast the middle and long-term runoff with a satisfactory accuracy. It has been proven that the forecast performance can be improved by using signal decomposition techniques to product more cleaner signals as model inputs. In this study, a new conjunction model (EEMD-neuro-fuzzy) with adaptive ability is proposed. The ensemble empirical model decomposition (EEMD) is used to decompose the runoff time series into several components, which are with different frequencies and more cleaner than the original time series. Then the neuro-fuzzy model is developed for each component. The final forecast results can be obtained by summing the outputs of all neuro-fuzzy models. Unlike the conventional forecast model, the decomposition and forecast models in this study are adjusted adaptively as long as new runoff information is added. The proposed models are applied to forecast the monthly runoff of Yichang station, located in Yangtze River of China. The results show that the performance of adaptive forecast model we proposed outperforms than the conventional forecast model, the Nash-Sutcliffe efficiency coefficient can reach to 0.9392. Due to its ability to process the nonstationary data, the forecast accuracy, especially in flood season, is improved significantly.

  4. Aproximación neuro-fuzzy para identificación de señales viales mediante tecnología infrarroja

    Directory of Open Access Journals (Sweden)

    G.N. Marichal

    2007-04-01

    Full Text Available Resumen: En este artículo se presenta un sistema basado en tecnología infrarroja para la clasificación de marcas viales empleando un sistema Neuro-Fuzzy como herramienta de clasificación. El sistema se ha testeado a partir de los datos suministrados cuando se ha instalado un prototipo en un robot móvil. Los resultados obtenidos son explicados en este artículo, haciendo hincapié en el diseño de nuevas reglas y la mejoría lograda mediante los métodos propuestos. Palabras clave: Control Inteligente, Robótica, Navegación de robots, Sistemas Neuro-Fuzzy

  5. Phase inductance estimation for switched reluctance motor using adaptive neuro-fuzzy inference system

    International Nuclear Information System (INIS)

    Daldaban, Ferhat; Ustkoyuncu, Nurettin; Guney, Kerim

    2006-01-01

    A new method based on an adaptive neuro-fuzzy inference system (ANFIS) for estimating the phase inductance of switched reluctance motors (SRMs) is presented. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of the ANFIS. The rotor position and the phase current of the 6/4 pole SRM are used to predict the phase inductance. The phase inductance results predicted by the ANFIS are in excellent agreement with the results of the finite element method

  6. Designing neuro-fuzzy controller for electromagnetic anti-lock braking system (ABS) on electric vehicle

    Science.gov (United States)

    Pramudijanto, Josaphat; Ashfahani, Andri; Lukito, Rian

    2018-03-01

    Anti-lock braking system (ABS) is used on vehicles to keep the wheels unlocked in sudden break (inside braking) and minimalize the stop distance of the vehicle. The problem of it when sudden break is the wheels locked so the vehicle steering couldn’t be controlled. The designed ABS system will be applied on ABS simulator using the electromagnetic braking. In normal condition or in condition without braking, longitudinal velocity of the vehicle will be equal with the velocity of wheel rotation, so the slip ratio will be 0 (0%) and if the velocity of wheel rotation is 0 (in locked condition) then the wheels will be slip 1 (100%). ABS system will keep the value of slip ratio so it will be 0.2 (20%). In this final assignment, the method that is used is Neuro-Fuzzy method to control the slip value on the wheels. The input is the expectable slip and the output is slip from plant. The learning algorithm which is used is Backpropagation that will work by feedforward to get actual output and work by feedback to get error value with target output. The network that was made based on fuzzy mechanism which are fuzzification, inference and defuzzification, Neuro-fuzzy controller can reduce overshoot plant respond to 43.2% compared to plant respond without controller by open loop.

  7. Prediction of Mechanical Properties of LDPE-TPS Nanocomposites Using Adaptive Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Maryam Sabetzadeh

    2012-12-01

    Full Text Available The changes in the behaviour of mechanical properties of low densitypolyethylene-thermoplastic corn starch (LDPE-TPCS nanocompositeswere studied by an adaptive neuro-fuzzy interference system. LDPE-TPCScomposites containing different quantities of nanoclay (Cloisite®15A, 0.5-3wt. % were prepared by extrusion process. In practice, it is difficult to carry out several experiments to identify the relationship between the extrusion process parameters and mechanical properties of the nanocomposites. In this paper, an adaptive neuro-fuzzy inference system (ANFIS was used for non-linear mapping between the processingparameters and the mechanical properties of LDPE-TPCS nanocomposites. ANFIS model due to possessing inference ability of fuzzy systems and also the learning feature of neural networks, could be used as a multiple inputs-multiple outputs to predict mechanical properties (such as ultimate tensile strength, elongation-at-break, Young’s modulus and relative impact strength of the nanocomposites. The proposed ANFIS model utilizes temperature, torque and Cloisite®15A contents as input parameters to predict the desired mechanical properties. The results obtained in this work indicatedthat ANFIS is an effective and intelligent method for prediction of the mechanical properties of the LDPE-TPCS nanocomposites with a good accuracy. The statistical quality of the ANFIS model was significant due to its acceptable mean square error criterion and good correlation coefficient (values > 0.8 between the experimental and simulated outputs.

  8. Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach.

    Science.gov (United States)

    Julie, E Golden; Selvi, S Tamil

    2016-01-01

    Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

  9. Modelo de auxílio à identificação de trabalho escravo ou degradante utilizando lógica fuzzy

    OpenAIRE

    Silva Filho, Benedito de Lima

    2006-01-01

    Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia de Produção Esta dissertação apresenta um modelo de auxílio de decisão na identificação de trabalho escravo ou degradante utilizando lógica fuzzy. O mesmo aumenta a transparência nas identificações das condições de trabalho escravo ou degradante, capacitando os agentes do Ministério do Trabalho e Emprego e os profissionais que atuam na área para uma melhor compreensã...

  10. Modeling the thermal behavior of fluid flow inside channels using an artificial locally linear neuro-fuzzy approach

    Directory of Open Access Journals (Sweden)

    Azadeh Hashemian

    2008-06-01

    Full Text Available Enhanced surface heat exchangers are commonly used all worldwide. If applicable, due to their complicated geometry, simulating corrugated plate heat exchangers is a time-consuming process. In the present study, first we simulate the heat transfer in a sharp V-shape corrugation cell with constant temperature walls; then, we use a Locally Linear Neuro-Fuzzy method based on a radial basis function (RBFs to model the temperature field in the whole channel. New approach is developed to deal with fast computational and low memory resources that can be used with the largest available data sets. The purpose of the research is to reveal the advantages of proposed Neuro-Fuzzy model as a powerful modeling system designed for predicting and to make a fair comparison between it and the successful FLUENT simulated approaches in its best structures.

  11. Mapa Hipertextual (MHTX): um modelo para organização hipertextual de documentos

    OpenAIRE

    Gercina Angela Borem de Oliveira Lima

    2004-01-01

    Este estudo visa a construção de um modelo estruturado semanticamente para auxiliar a organização e representação do conhecimento humano estruturado em hipertextos, baseado nas teorias da análise facetada e do mapa conceitual. O segundo passo neste estudo é a aplicação do modelo semântico para criar um protótipo chamado MAPA HIPERTEXTUAL (MHTX): um modelo para organização hipertextual de documentos que deverá ser utilizado para implementar a BTDECI UFMG (Biblioteca de Teses e Dissertações do...

  12. First experience from in-core sensor validation based on correlation and neuro-fuzzy techniques

    International Nuclear Information System (INIS)

    Figedy, S.

    2011-01-01

    In this work new types of nuclear reactor in-core sensor validation methods are outlined. The first one is based on combination of correlation coefficients and mutual information indices, which reflect the correlation of signals in linear and nonlinear regions. The method may be supplemented by wavelet transform based signal features extraction and pattern recognition by artificial neural networks and also fuzzy logic based decision making. The second one is based on neuro-fuzzy modeling of residuals between experimental values and their theoretical counterparts obtained from the reactor core simulator calculations. The first experience with this approach is described and further improvements to enhance the outcome reliability are proposed (Author)

  13. Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology

    Science.gov (United States)

    Petković, Dalibor; Shamshirband, Shahaboddin; Pavlović, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat

    2014-07-01

    The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

  14. Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

    Science.gov (United States)

    Mitra, Sunanda; Pemmaraju, Surya

    1992-01-01

    Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

  15. Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach

    Directory of Open Access Journals (Sweden)

    E. Golden Julie

    2016-01-01

    Full Text Available Wireless sensor networks (WSNs consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

  16. Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system

    Directory of Open Access Journals (Sweden)

    Doaa M. Atia

    2017-05-01

    Full Text Available The greenhouse is a complicated nonlinear system, which provides the plants with appropriate environmental conditions for growing. This paper presents a design of a control system for a greenhouse using geothermal energy as a power source for heating system. The greenhouse climate control problem is to create a favourable environment for the crop in order to reach predetermined results for high yield, high quality and low costs. Four controller techniques; PI control, fuzzy logic control, artificial neural network control and adaptive neuro-fuzzy control are used to adjust the greenhouse indoor temperature at the required value. MATLAB/SIMULINK is used to simulate the different types of controller techniques. Finally a comparative study between different control strategies is carried out.

  17. Classificador Neuro-nebuloso de Desempenho Escolar Usando o ANFIS com Implementação em R

    Directory of Open Access Journals (Sweden)

    Jeferson Costa da Silva

    2015-11-01

    Full Text Available Este artigo descreve alguns conceitos fundamentais para a implementação de um classificador neuro-nebuloso de desempenho escolar. Classificar informações de desempenho escolar é uma tarefa não-linear, pois envolve valores ("Atributos" como: nota, porcentagem de assiduidade (pontualidade e número de reprovações dos alunos em uma disciplina. O objetivo é qualificar o desempenho dos alunos em conceitos nebulosos ("Classes Nebulosas" como: ruim, bom ou ótimo. Baseado em informações numéricas contidas em um Dataset com os valores dos atributos é desenvolvido um classificador nebuloso baseado no modelo de ANFIS de aprendizagem e classificação neuro-nebulosa e implementado na linguagem de programação estatística R.

  18. Pendekatan Adaptive Neuro Fuzzy Sebagai Alternatif Bagi Bank Indonesia Dalam Menentukan Tingkat Inflasi Di Indonesia

    Directory of Open Access Journals (Sweden)

    Armaini Akhirson

    2016-10-01

    Full Text Available In uncertain economic like today, research and modeling the inflation rate is considered necessary to provide estimates and predictions of inflation rates in the future. Adaptive Neuro Fuzzy approach is a combination of  Neural Network and Fuzzy Logic. This study aims to describe the movement ofinflation(output variable  so it can beestimated by observing four Indonesia's macroeconomic data, namely the exchange rate, money supply, interbank interest rates, and the output gap (input variable. Observation period started from the data in 20011 to 20113. After the learning process is complete, fuzzy systems generate 45 fuzzy rules that can define the input-output behavior. The results of this study indicate a fairly high degree of accuracy with an average error rate is 0.5315.

  19. Adaptive neuro-fuzzy inference system for forecasting rubber milk production

    Science.gov (United States)

    Rahmat, R. F.; Nurmawan; Sembiring, S.; Syahputra, M. F.; Fadli

    2018-02-01

    Natural Rubber is classified as the top export commodity in Indonesia. Its high production leads to a significant contribution to Indonesia’s foreign exchange. Before natural rubber ready to be exported to another country, the production of rubber milk becomes the primary concern. In this research, we use adaptive neuro-fuzzy inference system (ANFIS) to do rubber milk production forecasting. The data presented here is taken from PT. Anglo Eastern Plantation (AEP), which has high data variance and range for rubber milk production. Our data will span from January 2009 until December 2015. The best forecasting result is 1,182% in term of Mean Absolute Percentage Error (MAPE).

  20. AplicaÃÃo da teoria dos nÃmeros difusos em um modelo de operaÃÃo de reservatÃrio, para estudar o comportamento da vazÃo regularizada e do rendimento.

    OpenAIRE

    SÃlvia Helena Lima dos Santos

    2008-01-01

    Este trabalho trata da aplicaÃÃo da Teoria Fuzzy em balanÃo hidrolÃgico de um reservatÃrio, para avaliar sua capacidade de previsÃo, na determinaÃÃo do cÃlculo do risco de falha de sistemas compostos por este tipo de corpo hÃdrico. No desenvolvimento da pesquisa, uma metodologia, transformando as equaÃÃes do balanÃo hidrolÃgico em equaÃÃes fuzzys, foi aplicada. ParÃmetros como tempo de esvaziamento e evaporaÃÃo foram considerados como funÃÃes de pertinÃncias dando, assim, ao modelo um carÃter...

  1. Design and experimental investigation of a decentralized GA-optimized neuro-fuzzy power system stabilizer

    Energy Technology Data Exchange (ETDEWEB)

    Talaat, Hossam E.A.; Abdennour, Adel; Al-Sulaiman, Abdulaziz A. [Electrical Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421 (Saudi Arabia)

    2010-09-15

    The aim of this research is the design and implementation of a decentralized power system stabilizer (PSS) capable of performing well for a wide range of variations in system parameters and/or loading conditions. The framework of the design is based on Fuzzy Logic Control (FLC). In particular, the neuro-fuzzy control rules are derived from training three classical PSSs; each is tuned using GA so as to perform optimally at one operating point. The effectiveness and robustness of the designed stabilizer, after implementing it to the laboratory model, is investigated. The results of real-time implementation prove that the proposed PSS offers a superior performance in comparison with the conventional stabilizer. (author)

  2. Adaptive neuro-fuzzy inference system based automatic generation control

    Energy Technology Data Exchange (ETDEWEB)

    Hosseini, S.H.; Etemadi, A.H. [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran)

    2008-07-15

    Fixed gain controllers for automatic generation control are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. So, to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to compute control gains. A control scheme based on artificial neuro-fuzzy inference system (ANFIS), which is trained by the results of off-line studies obtained using particle swarm optimization, is proposed in this paper to optimize and update control gains in real-time according to load variations. Also, frequency relaxation is implemented using ANFIS. The efficiency of the proposed method is demonstrated via simulations. Compliance of the proposed method with NERC control performance standard is verified. (author)

  3. Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system for prediction of financial and energy market data

    Directory of Open Access Journals (Sweden)

    A.K. Parida

    2016-09-01

    Full Text Available In this paper Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system is presented for the prediction and analysis of financial and electrical energy market data. The normally used TSK-type feedforward fuzzy neural network is unable to take the full advantage of the use of the linear fuzzy rule base in accurate input–output mapping and hence the consequent part of the rule base is made nonlinear using polynomial or arithmetic basis functions. Further the Chebyshev polynomial functions provide an expanded nonlinear transformation to the input space thereby increasing its dimension for capturing the nonlinearities and chaotic variations in financial or energy market data streams. Also the locally recurrent neuro-fuzzy information system (LRNFIS includes feedback loops both at the firing strength layer and the output layer to allow signal flow both in forward and backward directions, thereby making the LRNFIS mimic a dynamic system that provides fast convergence and accuracy in predicting time series fluctuations. Instead of using forward and backward least mean square (FBLMS learning algorithm, an improved Firefly-Harmony search (IFFHS learning algorithm is used to estimate the parameters of the consequent part and feedback loop parameters for better stability and convergence. Several real world financial and energy market time series databases are used for performance validation of the proposed LRNFIS model.

  4. Manifestation of a neuro-fuzzy model to produce landslide susceptibility map using remote sensing data derived parameters

    Science.gov (United States)

    Pradhan, Biswajeet; Lee, Saro; Buchroithner, Manfred

    Landslides are the most common natural hazards in Malaysia. Preparation of landslide suscep-tibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. In this study, a new attempt is tried to produce landslide susceptibility map of a part of Cameron Valley of Malaysia. This paper develops an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment for landslide susceptibility mapping. To ob-tain the neuro-fuzzy relations for producing the landslide susceptibility map, landslide locations were identified from interpretation of aerial photographs and high resolution satellite images, field surveys and historical inventory reports. Landslide conditioning factors such as slope, plan curvature, distance to drainage lines, soil texture, lithology, and distance to lineament were extracted from topographic, soil, and lineament maps. Landslide susceptible areas were analyzed by the ANFIS model and mapped using the conditioning factors. Furthermore, we applied various membership functions (MFs) and fuzzy relations to produce landslide suscep-tibility maps. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. Results show that, triangular, trapezoidal, and polynomial MFs were the best individual MFs for modelling landslide susceptibility maps (86

  5. An adaptive neuro fuzzy model for estimating the reliability of component-based software systems

    Directory of Open Access Journals (Sweden)

    Kirti Tyagi

    2014-01-01

    Full Text Available Although many algorithms and techniques have been developed for estimating the reliability of component-based software systems (CBSSs, much more research is needed. Accurate estimation of the reliability of a CBSS is difficult because it depends on two factors: component reliability and glue code reliability. Moreover, reliability is a real-world phenomenon with many associated real-time problems. Soft computing techniques can help to solve problems whose solutions are uncertain or unpredictable. A number of soft computing approaches for estimating CBSS reliability have been proposed. These techniques learn from the past and capture existing patterns in data. The two basic elements of soft computing are neural networks and fuzzy logic. In this paper, we propose a model for estimating CBSS reliability, known as an adaptive neuro fuzzy inference system (ANFIS, that is based on these two basic elements of soft computing, and we compare its performance with that of a plain FIS (fuzzy inference system for different data sets.

  6. An Adaptive Neuro-Fuzzy Inference System for Sea Level Prediction Considering Tide-Generating Forces and Oceanic Thermal Expansion

    Directory of Open Access Journals (Sweden)

    Li-Ching Lin Hsien-Kuo Chang

    2008-01-01

    Full Text Available The paper presents an adaptive neuro fuzzy inference system for predicting sea level considering tide-generating forces and oceanic thermal expansion assuming a model of sea level dependence on sea surface temperature. The proposed model named TGFT-FN (Tide-Generating Forces considering sea surface Temperature and Fuzzy Neuro-network system is applied to predict tides at five tide gauge sites located in Taiwan and has the root mean square of error of about 7.3 - 15.0 cm. The capability of TGFT-FN model is superior in sea level prediction than the previous TGF-NN model developed by Chang and Lin (2006 that considers the tide-generating forces only. The TGFT-FN model is employed to train and predict the sea level of Hua-Lien station, and is also appropriate for the same prediction at the tide gauge sites next to Hua-Lien station.

  7. Evaluation of the Application of Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems for Rainfall-Runoff Modelling in Zayandeh_rood Dam Basin

    Directory of Open Access Journals (Sweden)

    Mohammad Taghi Dastorani

    2012-01-01

    Full Text Available During recent few decades, due to the importance of the availability of water, and therefore the necesity of predicting run off resulted from rain fall there has been an increase in developing and implementation of new suitable method for prediction of run off using precipitation data. One of these approaches that have been developed in several areas of sciences including water related fields, is soft computing techniques such as artificial neural networks and fuzzy logic systems. This research was designed to evaluate the applicability of artificial neural network and adaptive neurofuzzy inference system to model rainfall-runoff process in Zayandeh_rood dam basin. It must be mentioned that, data have been analysed using Wingamma software, to select appropriate type and number of training input data before they can be used in the models. Then, it has been tried to evaluated applicability of artificial neural networks and neuro-fuzzy techniques to predict runoff generated from daily rainfall. Finally, the accuracy of the results produced by these methods has been compared using statistical criterion. Results taken from this research show that artificial neural networks and neuro-fuzzy technique presented different outputs in different conditions in terms of type and number of inputs variables, but both method have been able to produce acceptable results when suitable input variables and network structures are used.

  8. REPLACEMENT SPARE PART INVENTORY MONITORING USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

    Directory of Open Access Journals (Sweden)

    Hartono Hartono

    2016-01-01

    Full Text Available Abstract   The amount of inventory is determined on the basis of the demand. So that users can know the demand forecasts need to be done on the request. This study uses the data to implement a replacement parts on the electronic module production equipment in the telecommunications transmission systems, switching, access and power, ie by replacing the electronic module in the system is trouble  or damaged parts of a good electronic module spare parts inventory, while the faulty electronic modules shipped to the Repair Center for repaired again, so that the results of these improvements can replenish spare part  inventory. Parameters speed on improvement process of electronic module broken (repaired, in the form of an average repair time at the repair centers, in order to get back into the electronic module that is ready for used as spare parts in compliance with the safe supply inventory  warehouse.  This research using the method  of  Adaptive Neuro Fuzzy Inference System (ANFIS in developing a decision support system for inventory control of spare parts available in Warehouse Inventory taking into account several parameters supporters, namely demand, improvement and fulfillment of spare parts and repair time. This study uses a recycling input parameter repair faulty electronic module of the customer to immediately replace the module in inventory warehouse,  do improvements in the Repair Center. So the acceleration restoration factor is very influential as the input spare parts inventory supply in the warehouse and using the Adaptive Neuro-Fuzzy Inference System (ANFIS method.   Keywords: ANFIS, inventory control, replacement

  9. Estimating the Optimal Dosage of Sodium Valproate in Idiopathic Generalized Epilepsy with Adaptive Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Somayyeh Lotfi Noghabi

    2012-07-01

    Full Text Available Introduction: Epilepsy is a clinical syndrome in which seizures have a tendency to recur. Sodium valproate is the most effective drug in the treatment of all types of generalized seizures. Finding the optimal dosage (the lowest effective dose of sodium valproate is a real challenge to all neurologists. In this study, a new approach based on Adaptive Neuro-Fuzzy Inference System (ANFIS was presented for estimating the optimal dosage of sodium valproate in IGE (Idiopathic Generalized Epilepsy patients. Methods: 40 patients with Idiopathic Generalized Epilepsy, who were referred to the neurology department of Mashhad University of Medical Sciences between the years 2006-2011, were included in this study. The function Adaptive Neuro- Fuzzy Inference System (ANFIS constructs a Fuzzy Inference System (FIS whose membership function parameters are tuned (adjusted using either a back-propagation algorithm alone, or in combination with the least squares type of method (hybrid algorithm. In this study, we used hybrid method for adjusting the parameters. Methods: The R-square of the proposed system was %598 and the Pearson correlation coefficient was significant (P 0.05. Although the accuracy of the model was not high, it wasgood enough to be applied for treating the IGE patients with sodium valproate. Discussion: This paper presented a new application of ANFIS for estimating the optimal dosage of sodium valproate in IGE patients. Fuzzy set theory plays an important role in dealing with uncertainty when making decisions in medical applications. Collectively, it seems that ANFIS has a high capacity to be applied in medical sciences, especially neurology.

  10. Implementasi Adaptive Neuro-Fuzzy Inference System (Anfis untuk Peramalan Pemakaian Air di Perusahaan Daerah Air Minum Tirta Moedal Semarang

    Directory of Open Access Journals (Sweden)

    Ulfatun Hani'ah

    2016-06-01

    Full Text Available Peramalan pemakaian air pada bulan januari 2015 sampai April 2015 dapat dilakukan menggunakan perhitungan matematika dengan bantuan ilmu komputer. Metode yang digunakan adalah Adaptive Neuro Fuzzy Inference System (ANFIS dengan bantuan software MATLAB. Untuk pengujian program, dilakukan percobaan dengan memasukkan variabel klas = 2, maksimum epoh = 100, error = 10-6, rentang nilai learning rate = 0.6 sampai 0.9, dan rentang nilai momentum = 0.6 sampai 0.9. Simpulan yang diperoleh adalah bahwa implementasi metode Adaptive Neuro-Fuzzy Inference System dalam peramalan pemakaian air yang pertama adalah membuat rancangan flowchart, melakukan clustering data menggunakan fuzzy C-Mean, menentukan neuron tiap-tiap lapisan, mencari nilai parameter dengan menggunakan LSE rekursif, lalu penentuan perhitungan error menggunakan sum square error (SSE dan membuat sistem peramalan pemakaian air dengan software MATLAB. Setelah dilakukan percobaan hasil yang menunjukkan SSE paling kecil adalah nilai learning rate 0.9 dan momentum 0.6 dengan SSE 0.0080107. Hasil peramalan pemakaian air pada bulan Januari adalah 3.836.138m3, bulan Februari adalah 3.595.188m3, bulan Maret adalah 3.596.416 m3, dan bulan April adalah 3.776.833 m3. 

  11. Gas composition modeling in a reformed Methanol Fuel Cell system using adaptive Neuro-Fuzzy Inference Systems

    DEFF Research Database (Denmark)

    Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza

    2013-01-01

    This work presents a method for modeling the gas composition in a Reformed Methanol Fuel Cell system. The method is based on Adaptive Neuro-Fuzzy-Inference-Systems which are trained on experimental data. The developed models are of the H2, CO2, CO and CH3OH mass flows of the reformed gas. The ANFIS......, or fuel cell diagnostics systems....

  12. IDENTIFICANDO OS MODELOS DIDÁTICOS DE UM GRUPO DE PROFESSORES DE QUÍMICA

    Directory of Open Access Journals (Sweden)

    João Batista Santos Junior

    Full Text Available Esta investigação visa a identificar os modelos didáticos de um grupo de professores de Química de escolas públicas e propõe uma meto dologia para a sua utilização como instrumento de reflexão do docente em relação às suas concepções acerca do processo de ensino e aprendiza gem. Para tal, foi solicitado aos professores que respondessem a um ques tionário baseado nas ideias de Garcia Pérez (2000. Os resultados obtidos apontam que os professores combinam características diferentes dos modelos didáticos para formar um modelo didático eclético (GUIMARÃES, ECHEVERRÍA & MORAES, 2006. Esses modelos ecléticos encerram características antagônicas e podem indicar a necessi dade de aprofundamento da reflexão dos docentes sobre as suas concep ções. É proposta uma metodologia para a utilização do instrumento de investigação do pensamento do professor.

  13. Modeling of a HTPEM fuel cell using Adaptive Neuro-Fuzzy Inference Systems

    DEFF Research Database (Denmark)

    Justesen, Kristian Kjær; Andreasen, Søren Juhl; Sahlin, Simon Lennart

    2015-01-01

    In this work an Adaptive Neuro-Fuzzy Inference System (ANFIS) model of the voltage of a fuel cell is developed. The inputs of this model are the fuel cell temperature, current density and the carbon monoxide concentration of the anode supply gas. First an identification experiment which spans...... the expected operating range of the fuel cell is performed in a test station. The data from this experiment is then used to train ANFIS models with 2, 3, 4 and 5 membership functions. The performance of these models is then compared and it is found that using 3 membership functions provides the best compromise...

  14. Onisciente: um modelo de geração de contexto baseado em RFID e sensores

    Directory of Open Access Journals (Sweden)

    Felipe da Costa Noguez

    2015-04-01

    Full Text Available O monitoramento de entidades para determinar uma situação é uma aplicação interessante para diversas áreas. Pode-se utilizar dados obtidos de sensores, disponíveis em um ambiente, para descrever o contexto dos elementos presentes. Nesse âmbito, o presente trabalho tem como finalidade apresentar o Onisciente, um modelo que utiliza a informação de um conjunto de sensores que estejam monitorando um mesmo elemento para determinar o seu contexto e obter conhecimento sobre suas condições. Para isso, o Onisciente realiza a identificação dessas entidades, com o auxílio de uma leitora RFID acrescida de alguns sensores. No modelo, um contexto representa uma imagem de momento preciso no tempo, na qual se pode verificar fatos que estejam ocorrendo e, com isso, tomar decisões. Essas decisões envolvem classificar os contextos gerados e, conforme a classificação obtida, informar eventuais situações desfavoráveis às entidades monitoradas. Para determinar um contexto e realizar a classificação do significado que ele representa, são empregadas ontologias, computação sensível ao contexto e modelagem de contexto. Um protótipo foi desenvolvido para avaliar o modelo, mostrando a viabilidade do modelo proposto.

  15. Um modelo para analisar o problema de filas em caixas de supermercados: um estudo de caso

    Directory of Open Access Journals (Sweden)

    Reinaldo Morabito

    2000-06-01

    Full Text Available Neste estudo aplicamos teoria de filas para analisar o problema de congestão em caixas de supermercados. Estamos particularmente interessados em modelar o tempo médio de espera em fila, aqui entendido como uma componente importante do nível de serviço ao consumidor, em função da capacidade do sistema (i.e., o número de caixas num dado período de tempo. Três modelos são explorados: (i representar o sistema por meio de um simples modelo M/M/m de fila única, onde m é o número de caixas, (ii representar o sistema por meio de m modelos M/M/1 paralelos e independentes, e (iii representar o sistema por meio de um modelo Markoviano mais geral, onde os dois modelos anteriores podem ser vistos como casos particulares. Para avaliar o desempenho destes modelos, realizamos um estudo de caso num dos supermercados da rede Jaú-Serve, localizado no interior de São Paulo. Os resultados mostraram que o terceiro modelo pode resultar numa boa aproximação para o problema, ao contrário dos dois primeiros.In this study we applied queueing theory to analyze the problem of congestion in supermarket check-outs. We are particularly concerned with modeling the mean user waiting time in the queue, viewed as an important component of the user service, as a function of the capacity of the system (i.e., the number of cashiers in a given time period. Three models are explored: (i to represent the system by means of a simple M/M/m queueing model, where m is the number of cashiers, (ii to represent the system by means of m parallel and independent M/M/1 queueing models, and (iii to represent the system by means of a more generic Markovian model, where the former models can be viewed as particular cases. In order to evaluate the performance of the models, we developed a case study in one of the supermarkets of Jaú-Serve chain located in São Paulo State. The results showed that the third model can yield a good approximation for the problem, different from the

  16. Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model.

    Science.gov (United States)

    Rahman, Syed Masiur; Khondaker, A N; Khan, Rouf Ahmad

    2013-05-01

    In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.

  17. Neuro-fuzzy model for estimating race and gender from geometric distances of human face across pose

    Science.gov (United States)

    Nanaa, K.; Rahman, M. N. A.; Rizon, M.; Mohamad, F. S.; Mamat, M.

    2018-03-01

    Classifying human face based on race and gender is a vital process in face recognition. It contributes to an index database and eases 3D synthesis of the human face. Identifying race and gender based on intrinsic factor is problematic, which is more fitting to utilizing nonlinear model for estimating process. In this paper, we aim to estimate race and gender in varied head pose. For this purpose, we collect dataset from PICS and CAS-PEAL databases, detect the landmarks and rotate them to the frontal pose. After geometric distances are calculated, all of distance values will be normalized. Implementation is carried out by using Neural Network Model and Fuzzy Logic Model. These models are combined by using Adaptive Neuro-Fuzzy Model. The experimental results showed that the optimization of address fuzzy membership. Model gives a better assessment rate and found that estimating race contributing to a more accurate gender assessment.

  18. A Neuro-Fuzzy System for Characterization of Arm Movements

    Directory of Open Access Journals (Sweden)

    Alexandre Balbinot

    2013-02-01

    Full Text Available The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours.

  19. Hybrid neuro-fuzzy system for power generation control with environmental constraints

    International Nuclear Information System (INIS)

    Chaturvedi, Krishna Teerth; Pandit, Manjaree; Srivastava, Laxmi

    2008-01-01

    The real time controls at the central energy management centre in a power system, continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is least. However due to the strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained combined economic dispatch formulation is to estimate the optimal generation allocation to generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. Conventional optimization techniques are cumbersome for such complex optimization tasks and are not suitable for on-line use due to increased computational burden. This paper proposes a neuro-fuzzy power dispatch method where the uncertainty involved with power demand is modeled as a fuzzy variable. Then Levenberg-Marquardt neural network (LMNN) is used to evaluate the optimal generation schedules. This model trains almost hundred times faster that the popular BP neural network. The proposed method has been tested on two test systems and found to be suitable for on-line combined environmental economic dispatch

  20. Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields

    Directory of Open Access Journals (Sweden)

    Geraldo A. R. Ramos

    2017-06-01

    Full Text Available In this work, a neuro-fuzzy (NF simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL and the learning capability of neural network (NN to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques.

  1. CONSTRUINDO UM MODELO CURRICULAR INTEGRADO PARA A EJA/FIC

    Directory of Open Access Journals (Sweden)

    Raimundo Nonato O. Furtado

    2013-11-01

    Full Text Available Esse trabalho apresenta um modelo possível de desenvolvimento estrutural curricular integral, composto a partir da experiência dos autores, com o Projeto Mulheres Mil – Educação, Cidadania e Desenvolvimento Sustentável, Sub-Projeto “Desenvolvimento Comunitário: Impacto na Qualidade de Vida e Ambiental”, firmado pela parceria SETEC/MEC e o Group Collegia, do Quebec, Canadá. O Projeto Mulheres Mil foi inicialmente desenhado para execução da Rede Norte-Nordeste dos ex-CEFETs, hoje Institutos Federais, com execução a partir de 2008, no Instituto Federal da Paraíba, Campus João Pessoa. Teve o objetivo de apoiar, na prática, um dos modelos de políticas de inclusão social, revestido, na forma inicial, pelo Reconhecimento da Aprendizagem Prévia, tendo como alvo pessoas fora da faixa etária da escolaridade regular, trabalhadoras e, sem exceção, mulheres.

  2. Vivenciando a experiência da parturição em um modelo assistencial humanizado

    Directory of Open Access Journals (Sweden)

    Larissa Mandarano da Silva

    Full Text Available Tratou-se de um estudo qualitativo baseado na abordagem fenomenológica com o objetivo de compreender as experiências de puérperas que vivenciaram o trabalho de parto e o parto em um modelo assistencial humanizado. Os dados foram coletados em um hospital localizado na cidade de São Paulo, onde foram entrevistadas oito puérperas. Da análise dos dados surgiram os temas: Suportando o trabalho de parto e Tendo a oportunidade de resgatar a autonomia, e o fenômeno desvelado foi "Vivendo a ambiguidade da parturição em um modelo assistencial humanizado". Os relatos evidenciaram sentimentos como dor, medo e ansiedade, porém, possibilitou a participação e resgate da autonomia. Embora o estudo tenha sido realizado em um modelo assistencial considerado humanizado, as experiências das puérperas revelam que ainda se distanciam de uma efetiva humanização, conforme seus princípios. Este estudo pode ser utilizado para nortear ações educativas voltadas à humanização e gerar mudanças assistenciais

  3. Mapa digital de solos: uma proposta metodológica usando inferência fuzzy Digital soil map: a methodological proposal using fuzzy inference

    Directory of Open Access Journals (Sweden)

    Claudia C. Nolasco-Carvalho

    2009-02-01

    Full Text Available Elaborou-se um mapa digital de solos de uma área na região de Mucugê, BA, com o objetivo de avaliar o uso de geotecnologias na cartografia de solos. A metodologia desenvolvida a partir do modelo de inferência para solos - SoLIM , requer o conhecimento prévio da área por um especialista em mapeamento e está alicerçada na equação dos fatores de formação do solo e no modelo de distribuição dos solos na paisagem. Os dados, advindos do Modelo Digital do Terreno - MDT, da vegetação e da geologia, foram associados ao conhecimento do pedólogo e integrados em ambiente SIG (Sistema de Informações Geográficas sob inferência fuzzy. A modelagem por lógica fuzzy permitiu apontar as incertezas e transições da cobertura pedológica e gerou um mapa digital de solo que, quando comparado com o mapa convencional da área, mostrou menor generalização no domínio de espaços e parâmetros, ou seja, um refinamento da escala, porém a aplicabilidade da metodologia depende da validação de campo e da repetição em outras áreas.A digital soil map was elaborated for an area in the region of Mucugê-BA using data integration derived from a digital elevation model (DEM of the vegetation and geology that was associated with a soil scientist's knowledge and correlated in a GIS environment (Geography Information System under fuzzy inference, as a methodological proposal. The methodology was developed and based on the soil-land inference model - SoLIM, on the soil factor equation and the soil-landscape model. The fuzzy logic is able to simulate the uncertainty and transitions that often appear in pedologic systems. The results show that the methodology allows the generation of digital soil maps with increased scale and to reduce soil classe generalizations in the space and parameter domain. However, this methodology is very dependent upon the soil expert's knowledge and accuracy of the data base. To verify the applicability of the methodology the

  4. Modeling minimum temperature using adaptive neuro-fuzzy inference system based on spectral analysis of climate indices: A case study in Iran

    Directory of Open Access Journals (Sweden)

    Hojatollah Daneshmand

    2015-01-01

    Full Text Available Nowadays, a lot of attention is paid to the application of intelligent systems in predicting natural phenomena. Artificial neural network systems, fuzzy logic, and adaptive neuro-fuzzy inference are used in this field. Daily minimum temperature of the meteorology station of the city of Mashhad, in northeast of Iran, in a 42-year statistical period, 1966-2008, has been received from the Iranian meteorological organization. Adaptive neuro-fuzzy inference system is used for modeling and forecasting the monthly minimum temperature. To find appropriate inputs, three approaches, i.e. spectral analysis, correlation coefficient, and the knowledge of experts,are used. By applying fast Fourier transform to the parameter of monthly minimum temperature and climate indices, and by using correlation coefficient and the knowledge of experts, 3 indices, Nino 1 + 2, NP, and PNA, are selected as model inputs. A hybrid training algorithm is used to train the system. According to simulation results, a correlation coefficient of 0.987 between the observed values and the predicted values, as well as amean absolute percentage deviations of 27.6% indicate an acceptable estimation of the model.

  5. PSO based neuro fuzzy sliding mode control for a robot manipulator

    Directory of Open Access Journals (Sweden)

    M. Vijay

    2017-05-01

    Full Text Available This paper presents the control strategy of two degrees of freedom (2DOF rigid robot manipulator based on the coupling of artificial neuro fuzzy inference system (ANFIS with sliding mode control (SMC. Initially SMC with proportional integral derivative (PID sliding surface is adapted to control the robot manipulator. The parameters of the sliding surface are obtained by minimizing a quadratic performance indices using particle swarm optimization (PSO. Variations of SMC i.e. boundary sliding mode control (BSMC and boundary sliding mode control with PID sliding surface (PIDBSMC are developed for optimized performance index. Finally an ANFIS adaptive controller is proposed to generate the adaptive control signal and found to be more robust with regard to disturbances in input torque.

  6. Sub-module Short Circuit Fault Diagnosis in Modular Multilevel Converter Based on Wavelet Transform and Adaptive Neuro Fuzzy Inference System

    DEFF Research Database (Denmark)

    Liu, Hui; Loh, Poh Chiang; Blaabjerg, Frede

    2015-01-01

    for continuous operation and post-fault maintenance. In this article, a fault diagnosis technique is proposed for the short circuit fault in a modular multi-level converter sub-module using the wavelet transform and adaptive neuro fuzzy inference system. The fault features are extracted from output phase voltage...

  7. Hybrid clustering based fuzzy structure for vibration control - Part 1: A novel algorithm for building neuro-fuzzy system

    Science.gov (United States)

    Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok

    2015-01-01

    This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the joint input-output data space is presented. Crucial reason of this task is to overcome problems related to initialization and contradictory fuzzy rules, which usually happen when building ANFIS. The clustering process in the input data space is accomplished based on a proposed merging-possibilistic clustering (MPC) algorithm. The effectiveness of this process is evaluated to resume a clustering process in the joint input-output data space. The optimal parameters obtained after completion of the clustering process are used to build ANFIS. Simulations based on a numerical data, 'Daily Data of Stock A', and measured data sets of a smart damper are performed to analyze and estimate accuracy. In addition, convergence and robustness of the proposed algorithm are investigated based on both theoretical and testing approaches.

  8. Genetic-neuro-fuzzy system for grading depression

    Directory of Open Access Journals (Sweden)

    Kumar Ashish

    2018-01-01

    Full Text Available Main aim of this study is to develop a software prototype tool for grading and diagnosing depression that will help general physicians for first hand applications. Identification of key symptoms responsible for depression is also another important issue considered in this study. It involves collection of data taken from patients through doctors. Due to several reasons, collection of data in Indian scenario is extremely difficult and thus this tool will be very handy and useful for general physicians working at remote locations. Also, it is possible to collect a data pool through this software model. An intelligent Neuro-Fuzzy model is developed for this purpose. Performance of the said model has been optimized through two approaches. In Approach 1, where a back-propagation algorithm has been considered and in Approach 2, Genetic Algorithm has been used. The model is trained with 78 data and validated with 10 data. Approach 2 superseded Approach 1 in terms of diagnostic accuracy. Therefore, it can be said that the soft computing-based diagnostic models could assist the doctors to make informed decisions. Data for training and validation for this purpose has been collected during 2004–2005 from a Government mental hospital in India.

  9. Adaptive neuro-fuzzy inference system to improve the power quality of a split shaft microturbine power generation system

    Science.gov (United States)

    Oğuz, Yüksel; Üstün, Seydi Vakkas; Yabanova, İsmail; Yumurtaci, Mehmet; Güney, İrfan

    2012-01-01

    This article presents design of adaptive neuro-fuzzy inference system (ANFIS) for the turbine speed control for purpose of improving the power quality of the power production system of a split shaft microturbine. To improve the operation performance of the microturbine power generation system (MTPGS) and to obtain the electrical output magnitudes in desired quality and value (terminal voltage, operation frequency, power drawn by consumer and production power), a controller depended on adaptive neuro-fuzzy inference system was designed. The MTPGS consists of the microturbine speed controller, a split shaft microturbine, cylindrical pole synchronous generator, excitation circuit and voltage regulator. Modeling of dynamic behavior of synchronous generator driver with a turbine and split shaft turbine was realized by using the Matlab/Simulink and SimPowerSystems in it. It is observed from the simulation results that with the microturbine speed control made with ANFIS, when the MTPGS is operated under various loading situations, the terminal voltage and frequency values of the system can be settled in desired operation values in a very short time without significant oscillation and electrical production power in desired quality can be obtained.

  10. Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

    Science.gov (United States)

    Vasheghani Farahani, Jamileh; Zare, Mehdi; Lucas, Caro

    2012-04-01

    Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

  11. Applying a neuro-fuzzy approach for transient identification in a nuclear power plant

    International Nuclear Information System (INIS)

    Costa, Rafael G.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.; Carvalho, Paulo V.R.

    2009-01-01

    Transient identification in Nuclear Power Plant (NPP) is often a very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Several systems based on specialist systems, neural networks, and fuzzy logic have been developed for transient identification. In the work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A preliminary evaluation of the developed system was made at the Human-System Interface Laboratory (LABIHS). The obtained results show that the system can help the operators to take decisions during transients/accidents in the plant. (author)

  12. FPGA implementation of neuro-fuzzy system with improved PSO learning.

    Science.gov (United States)

    Karakuzu, Cihan; Karakaya, Fuat; Çavuşlu, Mehmet Ali

    2016-07-01

    This paper presents the first hardware implementation of neuro-fuzzy system (NFS) with its metaheuristic learning ability on field programmable gate array (FPGA). Metaheuristic learning of NFS for all of its parameters is accomplished by using the improved particle swarm optimization (iPSO). As a second novelty, a new functional approach, which does not require any memory and multiplier usage, is proposed for the Gaussian membership functions of NFS. NFS and its learning using iPSO are implemented on Xilinx Virtex5 xc5vlx110-3ff1153 and efficiency of the proposed implementation tested on two dynamic system identification problems and licence plate detection problem as a practical application. Results indicate that proposed NFS implementation and membership function approximation is as effective as the other approaches available in the literature but requires less hardware resources. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Lung Nodule Detection in CT Images using Neuro Fuzzy Classifier

    Directory of Open Access Journals (Sweden)

    M. Usman Akram

    2013-07-01

    Full Text Available Automated lung cancer detection using computer aided diagnosis (CAD is an important area in clinical applications. As the manual nodule detection is very time consuming and costly so computerized systems can be helpful for this purpose. In this paper, we propose a computerized system for lung nodule detection in CT scan images. The automated system consists of two stages i.e. lung segmentation and enhancement, feature extraction and classification. The segmentation process will result in separating lung tissue from rest of the image, and only the lung tissues under examination are considered as candidate regions for detecting malignant nodules in lung portion. A feature vector for possible abnormal regions is calculated and regions are classified using neuro fuzzy classifier. It is a fully automatic system that does not require any manual intervention and experimental results show the validity of our system.

  14. Short-term and long-term thermal prediction of a walking beam furnace using neuro-fuzzy techniques

    Directory of Open Access Journals (Sweden)

    Banadaki Hamed Dehghan

    2015-01-01

    Full Text Available The walking beam furnace (WBF is one of the most prominent process plants often met in an alloy steel production factory and characterized by high non-linearity, strong coupling, time delay, large time-constant and time variation in its parameter set and structure. From another viewpoint, the WBF is a distributed-parameter process in which the distribution of temperature is not uniform. Hence, this process plant has complicated non-linear dynamic equations that have not worked out yet. In this paper, we propose one-step non-linear predictive model for a real WBF using non-linear black-box sub-system identification based on locally linear neuro-fuzzy (LLNF model. Furthermore, a multi-step predictive model with a precise long prediction horizon (i.e., ninety seconds ahead, developed with application of the sequential one-step predictive models, is also presented for the first time. The locally linear model tree (LOLIMOT which is a progressive tree-based algorithm trains these models. Comparing the performance of the one-step LLNF predictive models with their associated models obtained through least squares error (LSE solution proves that all operating zones of the WBF are of non-linear sub-systems. The recorded data from Iran Alloy Steel factory is utilized for identification and evaluation of the proposed neuro-fuzzy predictive models of the WBF process.

  15. ESTUDO DO RISCO DE DEGRADAÇÃO AMBIENTAL EM RIOS MEDIANTE A APLICAÇÃO DE UM MODELO MATEMÁTICO fuzzy DE DBO/OD, CONSIDERANDO DIFERENTES FONTES DE LANÇAMENTO DE EFLUENTES

    Directory of Open Access Journals (Sweden)

    Ada Amelia Lopes

    2016-06-01

    Full Text Available Com base no fenômeno de transferência de massa, a lógica fuzzy desenvolvida no presente estudo estabeleceu relações entre a Demanda Bioquímica de Oxigênio (DBO e o Oxigênio Dissolvido (OD, para diferentes lançamentos. A aplicação da lógica fuzzy neste cenário, permitiu que as incertezas das variáveis envolvidas no processo, especificamente na solução da equação de advecção, fossem avaliadas e o risco de contaminação de um sistema hídrico diante dos lançamentos de poluentes pudesse ser investigado. No desenvolvimento da lógica fuzzy,  os parâmetros de DBO/OD foram convertidos em um conjunto de equações diferenciais e representados como funçõe de pertinência. Para execução das simulações da qualidade da água, os autores utilizaram um programa computacional codificado em linguagem FORTRAN. De acordo com os resultados, concluiu-se que a combinação da Teoria do Transporte de Massa e a da Lógica fuzzy pode ser uma importante ferramenta para a avaliação da Análise de Risco em sistemas hídricos, contribuindo sobremaneira para o monitoramento de temáticas prioritárias da Engenharia Ambiental.

  16. Adaptive neuro fuzzy system for modelling and prediction of distance pantograph catenary in railway transportation

    Science.gov (United States)

    Panoiu, M.; Panoiu, C.; Lihaciu, I. L.

    2018-01-01

    This research presents an adaptive neuro-fuzzy system which is used in the prediction of the distance between the pantograph and contact line of the electrical locomotives used in railway transportation. In railway transportation any incident that occurs in the electrical system can have major negative effects: traffic interrupts, equipment destroying. Therefore, a prediction as good as possible of such situations is very useful. In the paper was analyzing the possibility of modeling and prediction the variation of the distance between the pantograph and the contact line using intelligent techniques

  17. Hermes: um modelo para acessibilidade ubíqua dedicado à deficiência auditiva

    Directory of Open Access Journals (Sweden)

    Carlos Felipe Rocha Carneiro

    2016-11-01

    Full Text Available Nota-se, atualmente, que o avanço da tecnologia e a crescente quantidade de dispositivos móveis vêm estimulando o uso desse tipo de tecnologia. Porém, esses dispositivos não estão prontos para atender pessoas com determinados tipos de deficiência, especificamente o deficiente auditivo. O presente trabalho tem por objetivo propor um modelo de suporte ao deficiente auditivo chamado Hermes. O Hermes no que diz respeito aos trabalhos relacionados é o único a apresentar sensibilidade ao contexto, pois nenhum dos modelos avaliados tem essa característica. Além disso, Hermes suporta o reconhecimento de som, a localização de recurso e, por fim, demonstra suporte a trilhas. A avaliação do modelo foi baseada em cenários, partindo de um recurso selecionado pelo usuário, mostrando que o aplicativo oferece suporte para acessibilidade. O Hermes foi avaliado por dez usuários, um deles deficiente auditivo. Os avaliadores aprovaram com 88% o aplicativo no quesito que buscou avaliar a facilidade percebida de uso, e com 90% o quesito de utilidade percebida.

  18. Um modelo de programação orientado ao desenvolvimento de sistemas ubíquos

    Directory of Open Access Journals (Sweden)

    Alex Sandro Garzão

    2014-04-01

    Full Text Available A tarefa de desenvolver aplicações ubíquas nos modelos tradicionais de programação torna-se um desafio pois a maioria desses modelos baseia-se em premissas estáticas de arquitetura, dados, aplicação e sistemas operacionais. Por isso o presente trabalho propõe o \\textit{Ubiquitous Oriented Programming} (abreviadamente UOP, um modelo de programação orientado ao desenvolvimento de sistemas ubíquos. O UOP utiliza os conceitos de serviços e da programação orientada a objetos, integrando-os com requisitos necessários em aplicações ubíquas como compartilhamento de informações contextuais, sensibilidade ao contexto, adaptação ao contexto, mobilidade de código e concorrência. O ambiente do UOP é composto por uma linguagem de programação (UbiL, um compilador (UbiC e por uma máquina virtual (UbiVM que suporta a execução das aplicações desenvolvidas em UbiL. O modelo foi avaliado de forma experimental, onde uma aplicação foi criada e então simulada através de um cenário de comércio ubíquo. Através desse experimento concluiu-se que o UOP facilitou o desenvolvimento dessa aplicação.

  19. An adaptive neuro-fuzzy controller for mold level control in continuous casting

    International Nuclear Information System (INIS)

    Zolghadri Jahromi, M.; Abolhassan Tash, F.

    2001-01-01

    Mold variations in continuous casting are believed to be the main cause of surface defects in the final product. Although a Pid controller is well capable of controlling the level under normal conditions, it cannot prevent large variations of mold level when a disturbance occurs in the form of nozzle unclogging. In this paper, dual controller architecture is presented, a Pid controller is used as the main controller of the plant and an adaptive neuro-fuzzy controller is used as an auxiliary controller to help the Pid during disturbed phases. The control is passed back to the Pid controller after the disturbance is being dealt with. Simulation results prove the effectiveness of this control strategy in reducing mold level variations during the unclogging period

  20. Um objeto-modelo didático do movimento aparente do Sol em relação ao fundo de estrelas

    Directory of Open Access Journals (Sweden)

    Francisco Catelli

    2013-04-01

    “Por que o Sol nunca passa pela constelação de Órion?” A resposta a essa pergunta, feita por um aluno, inicia com uma revisão da literatura sobre modelos, em especial aqueles relacionados aos objetos-modelo didáticos. A pergunta é respondida, então, inicialmente, usando um mapa do céu, o qual é, em seguida, transformado em um cilindro, e este, por sua vez, é montado em um dispositivo didático de modo a materializar o movimento aparente da abóbada celeste, em uma latitude escolhida, constituindo-se, assim, em um verdadeiro “mini planetário”. Contudo, para chegar a um nível de resposta que inclua conceitos como o da vertical do lugar, a invariância da orientação do eixo de rotação da Terra, o equador celeste e a eclíptica, fazem-se necessários outros modelos. Por fim, conclui-se que não é apenas um, mas sim um conjunto articulado de modelos que pode levar a uma resposta aceitável à questão proposta.

  1. Um modelo alternativo para a quantificação de multiprodutos em árvores individuais

    OpenAIRE

    Soares,Thelma Shirlen; Leite,Helio Garcia; Vale,Antônio Bartolomeu do

    2004-01-01

    Neste estudo foram conduzidos testes preliminares com o objetivo de avaliar um modelo de taper, proposto a partir da adaptação dos modelos desenvolvidos por Ormerod (1973) e Turnbull (1979), caracterizados pela sua simplicidade e facilidade de ajuste. Em comparação com modelos já consagrados, observou-se que o modelo alternativo apresentou consistência nos ajustes realizados. Portanto, pode-se concluir que o modelo apresentado é recomendável para a quantificação de multiprodutos de árvores in...

  2. Adaptive Neuro-fuzzy Inference System as Cache Memory Replacement Policy

    Directory of Open Access Journals (Sweden)

    CHUNG, Y. M.

    2014-02-01

    Full Text Available To date, no cache memory replacement policy that can perform efficiently for all types of workloads is yet available. Replacement policies used in level 1 cache memory may not be suitable in level 2. In this study, we focused on developing an adaptive neuro-fuzzy inference system (ANFIS as a replacement policy for improving level 2 cache performance in terms of miss ratio. The recency and frequency of referenced blocks were used as input data for ANFIS to make decisions on replacement. MATLAB was employed as a training tool to obtain the trained ANFIS model. The trained ANFIS model was implemented on SimpleScalar. Simulations on SimpleScalar showed that the miss ratio improved by as high as 99.95419% and 99.95419% for instruction level 2 cache, and up to 98.04699% and 98.03467% for data level 2 cache compared with least recently used and least frequently used, respectively.

  3. A Genetic-Neuro-Fuzzy inferential model for diagnosis of tuberculosis

    Directory of Open Access Journals (Sweden)

    Mumini Olatunji Omisore

    2017-01-01

    Full Text Available Tuberculosis is a social, re-emerging infectious disease with medical implications throughout the globe. Despite efforts, the coverage of tuberculosis disease (with HIV prevalence in Nigeria rose from 2.2% in 1991 to 22% in 2013 and the orthodox diagnosis methods available for Tuberculosis diagnosis were been faced with a number of challenges which can, if measure not taken, increase the spread rate; hence, there is a need for aid in diagnosis of the disease. This study proposes a technique for intelligent diagnosis of TB using Genetic-Neuro-Fuzzy Inferential method to provide a decision support platform that can assist medical practitioners in administering accurate, timely, and cost effective diagnosis of Tuberculosis. Performance evaluation observed, using a case study of 10 patients from St. Francis Catholic Hospital Okpara-In-Land (Delta State, Nigeria, shows sensitivity and accuracy results of 60% and 70% respectively which are within the acceptable range of predefined by domain experts.

  4. A controladoria utilizando a lógica fuzzy no auxílio à empresa para definição das prioridades do planejamento estratégico: um estudo em uma empresa de turismo

    Directory of Open Access Journals (Sweden)

    Adolfo Alberto Vanti

    2007-03-01

    Full Text Available Este artigo demonstra o resultado de uma pesquisa que apresenta a lógica fuzzy contribuindo com a controladoria na formulação e avaliação das estratégias organizacionais. O objetivo deste trabalho é apresentar a lógica fuzzy em uma aplicação empresarial como uma técnica de análise e julgamento para a tomada de decisão, capaz de auxiliar os gestores e a controladoria na modelagem de indicadores e de estratégias organizacionais. As metodologias utilizadas, para embasar os pressupostos deste trabalho, são duas: uma pesquisa bibliográfica e um estudo de caso aplicado numa empresa do setor de turismo, localizada no Estado do Rio Grande do Sul, Brasil. Neste estudo, as variáveis serão analisadas por meio da aplicação do modelo de administração lógica, desenvolvido por Espín, Becker e Vanti (2005, sistema este embasado nos pressupostos da lógica fuzzy. Ao final da investigação, demonstra-se que a lógica fuzzy pode ser um instrumento utilizado com sucesso pela controladoria na construção e visualização da priorização dos objetivos e das ações estratégicas.

  5. Fukunaga-Koontz feature transformation for statistical structural damage detection and hierarchical neuro-fuzzy damage localisation

    Science.gov (United States)

    Hoell, Simon; Omenzetter, Piotr

    2017-07-01

    Considering jointly damage sensitive features (DSFs) of signals recorded by multiple sensors, applying advanced transformations to these DSFs and assessing systematically their contribution to damage detectability and localisation can significantly enhance the performance of structural health monitoring systems. This philosophy is explored here for partial autocorrelation coefficients (PACCs) of acceleration responses. They are interrogated with the help of the linear discriminant analysis based on the Fukunaga-Koontz transformation using datasets of the healthy and selected reference damage states. Then, a simple but efficient fast forward selection procedure is applied to rank the DSF components with respect to statistical distance measures specialised for either damage detection or localisation. For the damage detection task, the optimal feature subsets are identified based on the statistical hypothesis testing. For damage localisation, a hierarchical neuro-fuzzy tool is developed that uses the DSF ranking to establish its own optimal architecture. The proposed approaches are evaluated experimentally on data from non-destructively simulated damage in a laboratory scale wind turbine blade. The results support our claim of being able to enhance damage detectability and localisation performance by transforming and optimally selecting DSFs. It is demonstrated that the optimally selected PACCs from multiple sensors or their Fukunaga-Koontz transformed versions can not only improve the detectability of damage via statistical hypothesis testing but also increase the accuracy of damage localisation when used as inputs into a hierarchical neuro-fuzzy network. Furthermore, the computational effort of employing these advanced soft computing models for damage localisation can be significantly reduced by using transformed DSFs.

  6. Using adaptive neuro-fuzzy inference system technique for crosstalk correction in simultaneous {sup 99m}Tc/{sup 201}Tl SPECT imaging: A Monte Carlo simulation study

    Energy Technology Data Exchange (ETDEWEB)

    Heidary, Saeed, E-mail: saeedheidary@aut.ac.ir; Setayeshi, Saeed, E-mail: setayesh@aut.ac.ir

    2015-01-11

    This work presents a simulation based study by Monte Carlo which uses two adaptive neuro-fuzzy inference systems (ANFIS) for cross talk compensation of simultaneous {sup 99m}Tc/{sup 201}Tl dual-radioisotope SPECT imaging. We have compared two neuro-fuzzy systems based on fuzzy c-means (FCM) and subtractive (SUB) clustering. Our approach incorporates eight energy-windows image acquisition from 28 keV to 156 keV and two main photo peaks of {sup 201}Tl (77±10% keV) and {sup 99m}Tc (140±10% keV). The Geant4 application in emission tomography (GATE) is used as a Monte Carlo simulator for three cylindrical and a NURBS Based Cardiac Torso (NCAT) phantom study. Three separate acquisitions including two single-isotopes and one dual isotope were performed in this study. Cross talk and scatter corrected projections are reconstructed by an iterative ordered subsets expectation maximization (OSEM) algorithm which models the non-uniform attenuation in the projection/back-projection. ANFIS-FCM/SUB structures are tuned to create three to sixteen fuzzy rules for modeling the photon cross-talk of the two radioisotopes. Applying seven to nine fuzzy rules leads to a total improvement of the contrast and the bias comparatively. It is found that there is an out performance for the ANFIS-FCM due to its acceleration and accurate results.

  7. Macroscopic Rock Texture Image Classification Using a Hierarchical Neuro-Fuzzy Class Method

    Directory of Open Access Journals (Sweden)

    Laercio B. Gonçalves

    2010-01-01

    Full Text Available We used a Hierarchical Neuro-Fuzzy Class Method based on binary space partitioning (NFHB-Class Method for macroscopic rock texture classification. The relevance of this study is in helping Geologists in the diagnosis and planning of oil reservoir exploration. The proposed method is capable of generating its own decision structure, with automatic extraction of fuzzy rules. These rules are linguistically interpretable, thus explaining the obtained data structure. The presented image classification for macroscopic rocks is based on texture descriptors, such as spatial variation coefficient, Hurst coefficient, entropy, and cooccurrence matrix. Four rock classes have been evaluated by the NFHB-Class Method: gneiss (two subclasses, basalt (four subclasses, diabase (five subclasses, and rhyolite (five subclasses. These four rock classes are of great interest in the evaluation of oil boreholes, which is considered a complex task by geologists. We present a computer method to solve this problem. In order to evaluate system performance, we used 50 RGB images for each rock classes and subclasses, thus producing a total of 800 images. For all rock classes, the NFHB-Class Method achieved a percentage of correct hits over 73%. The proposed method converged for all tests presented in the case study.

  8. Modeling and simulation of adaptive Neuro-fuzzy based intelligent system for predictive stabilization in structured overlay networks

    Directory of Open Access Journals (Sweden)

    Ramanpreet Kaur

    2017-02-01

    Full Text Available Intelligent prediction of neighboring node (k well defined neighbors as specified by the dht protocol dynamism is helpful to improve the resilience and can reduce the overhead associated with topology maintenance of structured overlay networks. The dynamic behavior of overlay nodes depends on many factors such as underlying user’s online behavior, geographical position, time of the day, day of the week etc. as reported in many applications. We can exploit these characteristics for efficient maintenance of structured overlay networks by implementing an intelligent predictive framework for setting stabilization parameters appropriately. Considering the fact that human driven behavior usually goes beyond intermittent availability patterns, we use a hybrid Neuro-fuzzy based predictor to enhance the accuracy of the predictions. In this paper, we discuss our predictive stabilization approach, implement Neuro-fuzzy based prediction in MATLAB simulation and apply this predictive stabilization model in a chord based overlay network using OverSim as a simulation tool. The MATLAB simulation results present that the behavior of neighboring nodes is predictable to a large extent as indicated by the very small RMSE. The OverSim based simulation results also observe significant improvements in the performance of chord based overlay network in terms of lookup success ratio, lookup hop count and maintenance overhead as compared to periodic stabilization approach.

  9. Propose a Model for Customer Purchase Decision in B2C Websites Using Adaptive Neuro-Fuzzy Inference System

    OpenAIRE

    Mehrbakhsh Nilashi, Mohammad Fathian, Mohammad Reza Gholamian, Othman bin Ibrahim

    2011-01-01

    If companies are to enjoy long-term success in the Internet marketplace, they must effectivelymanage the complex, multidimensional process of building online consumer trust. The onlineenvironment and the quality and usability of websites help the browser and consumer to beattracted and accessible to the information and the product and services available online. In thisPaper a new model would be suggested based on neuro-fuzzy System which depicts some of thehidden relationships between the cri...

  10. Speed control of permanent magnet excitation transverse flux linear motor by using adaptive neuro-fuzzy controller

    Energy Technology Data Exchange (ETDEWEB)

    Hasanien, Hany M., E-mail: Hanyhasanien@ieee.or [Dept. of Elec. Power and Machines, Faculty of Eng., Ain-shams Univ. Cairo (Egypt); Muyeen, S.M. [Department of Electrical Engineering, Petroleum Institute, Abu Dhabi (United Arab Emirates); Tamura, Junji [Department of EEE, Kitami Institute of Technology, 165 Koen Cho, Kitami 090-8507, Hokkaido (Japan)

    2010-12-15

    This paper presents a novel adaptive neuro-fuzzy controller applies on transverse flux linear motor for controlling its speed. The proposed controller presents fuzzy logic controller with self tuning scaling factors based on artificial neural network structure. It has two input variables and one control output variable. Firstly the fuzzy logic control rules are described then NN architecture is represented to self tune the output scaling factors of the controller. The application of this control technique represents the novelty of work, where this algorithm has so far not been stated before for this type of drives. This methodology solves the problem of nonlinearities and load changes of TFLM drives. The dynamic response of the motor is studied under the rated load condition as well as load disturbances. The proposed controller ensures fast and accurate dynamic response with an excellent steady state performance. The dynamic response of the motor with the proposed controller is compared with PI and adaptive NN controllers. It is found that the proposed controller gives better and faster response from the viewpoint of overshoot and settling time. Matlab/Simulink tool is used for this dynamic simulation study.

  11. Potential of neuro-fuzzy methodology to estimate noise level of wind turbines

    Science.gov (United States)

    Nikolić, Vlastimir; Petković, Dalibor; Por, Lip Yee; Shamshirband, Shahaboddin; Zamani, Mazdak; Ćojbašić, Žarko; Motamedi, Shervin

    2016-01-01

    Wind turbines noise effect became large problem because of increasing of wind farms numbers since renewable energy becomes the most influential energy sources. However, wind turbine noise generation and propagation is not understandable in all aspects. Mechanical noise of wind turbines can be ignored since aerodynamic noise of wind turbine blades is the main source of the noise generation. Numerical simulations of the noise effects of the wind turbine can be very challenging task. Therefore in this article soft computing method is used to evaluate noise level of wind turbines. The main goal of the study is to estimate wind turbine noise in regard of wind speed at different heights and for different sound frequency. Adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the wind turbine noise levels.

  12. Modelo conceptual de datos difusos de triaje para emergencia hospitalaria representado con FuzzyEER

    Directory of Open Access Journals (Sweden)

    Wuilfredo Rangel

    2010-05-01

    Full Text Available El triaje de emergencia persigue desarrollar el proceso de valorización clínica preliminar para ordenar los pacientes según el nivel de urgencia o gravedad, antes de la atención médica, de forma que en una condición de saturación del servicio o disminución de recursos, los pacientes más graves sean tratados con prioridad. Un proceso de triaje presenta un alto grado de imprecisión o incertidumbre al momento de expresar el estado de salud de un paciente, en consecuencia puede haber dificultad y riesgo en la clasificación de las urgencias. En los servicios de emergencias de loshospitales públicos venezolanos, en especial el servicio de emergencia del Hospital Universitario de Caracas (HUC, seha evidenciado un incremento en el número de casos que se atienden, siendo una constante la saturación del servicio, lo cual imposibilita que los pacientes con prioridad de atención, sean tratados más rápidamente de forma oportuna y eficaz. En este trabajo se propone un modelo conceptual de datos difuso de triaje para el sector salud venezolano basado en el proceso de triaje hospitalario de la Sociedad Venezolana de Medicina de Emergencia y Desastres (SVMED, ya que un modelo de datos que contemple la representación de atributos difusos puede representar de mejor forma los requerimientos del sistema. El modelo propuesto utiliza la notación Entidad Relación con elementos difusos (FuzzyEER para la representación de las entidades y atributos con imprecisión. Dicho modelo será la base de un sistema automatizado de triaje que hará uso del lenguaje de consultas difusas FSQL (Fuzzy SQL.

  13. Neuro-fuzzy control strategy for an offshore steel jacket platform subjected to wave-induced forces using magneto rheological dampers

    International Nuclear Information System (INIS)

    Sarrafan, Atabak; Zareh, Seiyed Hamid; Khayyat, Amir Ali Akbar; Zabihollah, Abolghassem

    2012-01-01

    Magnetorheological (MR) damper is a prominent semi-active control device to vibrate mitigation of structures. Due to the inherent non-linear nature of MR damper, an intelligent non-linear neuro-fuzzy control strategy is designed to control wave-induced vibration of an offshore steel jacket platform equipped with MR dampers. In the proposed control system, a dynamic-feedback neural network is adapted to model non-linear dynamic system, and the fuzzy logic controller is used to determine the control forces of MR dampers. By use of two feed forward neural networks required voltages and actual MR damper forces are obtained, in which the first neural network and the second one acts as the inverse dynamics model, and the forward dynamics model of the MR dampers, respectively. The most important characteristic of the proposed intelligent control strategy is its inherent robustness and its ability to handle the non-linear behavior of the system. Besides, no mathematical model needed to calculate forces produced by MR dampers. According to linearized Morison equation, wave-induced forces are determined. The performance of the proposed neuro-fuzzy control system is compared with that of a traditional semi-active control strategy, i.e., clipped optimal control system with LQG-target controller, through computer simulations, while the uncontrolled system response is used as the baseline. It is demonstrated that the design of proposed control system framework is more effective than that of the clipped optimal control scheme with LQG-target controller to reduce the vibration of offshore structure. Furthermore, the control strategy is very important for semi-active control

  14. Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses.

    Science.gov (United States)

    Mathur, Neha; Glesk, Ivan; Buis, Arjan

    2016-10-01

    Monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used impeding the required consistent positioning of the temperature sensors during donning and doffing. Predicting the in-socket residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. In this work, we propose to implement an adaptive neuro fuzzy inference strategy (ANFIS) to predict the in-socket residual limb temperature. ANFIS belongs to the family of fused neuro fuzzy system in which the fuzzy system is incorporated in a framework which is adaptive in nature. The proposed method is compared to our earlier work using Gaussian processes for machine learning. By comparing the predicted and actual data, results indicate that both the modeling techniques have comparable performance metrics and can be efficiently used for non-invasive temperature monitoring. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  15. UM MODELO MARKOVIANO DE DECISÃO PARA A OTIMIZAÇÃO DE UM SISTEMA DE MANUTENÇÃO COM TEMPOS DE REPARO COXIANOS E FASES NÃO OBSERVÁVEIS

    Directory of Open Access Journals (Sweden)

    Rodrigues Rita de Cássia Meneses

    2001-01-01

    Full Text Available Neste artigo analisa-se um sistema de manutenção de máquinas com dois servidores diferentes, tempos até a quebra das máquinas exponencialmente distribuídos e tempos de reparo seguindo uma distribuição do tipo fase com configuração de Cox. Consideram-se dois modelos que se diferenciam pela possibilidade ou não de se observar as fases das distribuições do tipo fase: um modelo com informação completa e um modelo com informação parcial. No primeiro caso, modela-se o sistema por um processo markoviano de decisão a tempo contínuo e no segundo por um processo markoviano de decisão com informação parcial, conforme este é definido em Hordijk & Loeve (1994. Resultados numéricos são apresentados

  16. Design of a biped locomotion controller based on adaptive neuro-fuzzy inference systems

    Energy Technology Data Exchange (ETDEWEB)

    Shieh, M-Y; Chang, K-H [Department of E. E., Southern Taiwan University, 1 Nantai St., YungKang City, Tainan County 71005, Taiwan (China); Lia, Y-S [Executive Director Office, ITRI, Southern Taiwan Innovation Park, Tainan County, Taiwan (China)], E-mail: myshieh@mail.stut.edu.tw

    2008-02-15

    This paper proposes a method for the design of a biped locomotion controller based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) inverse learning model. In the model developed here, an integrated ANFIS structure is trained to function as the system identifier for the modeling of the inverse dynamics of a biped robot. The parameters resulting from the modeling process are duplicated and integrated as those of the biped locomotion controller to provide favorable control action. As the simulation results show, the proposed controller is able to generate a stable walking cycle for a biped robot. Moreover, the experimental results demonstrate that the performance of the proposed controller is satisfactory under conditions when the robot stands in different postures or moves on a rugged surface.

  17. Design of a biped locomotion controller based on adaptive neuro-fuzzy inference systems

    International Nuclear Information System (INIS)

    Shieh, M-Y; Chang, K-H; Lia, Y-S

    2008-01-01

    This paper proposes a method for the design of a biped locomotion controller based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) inverse learning model. In the model developed here, an integrated ANFIS structure is trained to function as the system identifier for the modeling of the inverse dynamics of a biped robot. The parameters resulting from the modeling process are duplicated and integrated as those of the biped locomotion controller to provide favorable control action. As the simulation results show, the proposed controller is able to generate a stable walking cycle for a biped robot. Moreover, the experimental results demonstrate that the performance of the proposed controller is satisfactory under conditions when the robot stands in different postures or moves on a rugged surface

  18. Zero NDZ assessment for anti-islanding protection using wavelet analysis and neuro-fuzzy system in inverter based distributed generation

    International Nuclear Information System (INIS)

    Shayeghi, H.; Sobhani, B.

    2014-01-01

    Highlights: • Reduction of NDZ nearly to zero by proposed passive time–frequency islanding detection algorithm. • Avoiding of threshold selection based on neuro-fuzzy learning system. • Unchanged of power quality against active detection techniques. • Separate islanding condition from other switching condition. - Abstract: Due to increase of electrical power demand, several uncommon sources mainly voltage source converter (VSC) based distributed generations (DGs) have been included into the power systems which increased the systems complexity and uncertainty. One of the most problem of DGs is unwanted islanding. This paper addresses a reliable passive time–frequency islanding detection algorithm using the multi signal analysis method. In addition, Adaptive Neuro Fuzzy Learning System (ANFIS) is used for decision making mechanism to avoid of threshold. Reduction of non detection zone (NDZ) is another contribution of this study. At first, all possible linear and nonlinear load switching, motor starting, capacitor bank switching, and islanding conditions are simulated and the required detection parameters measured. Using the discrete wavelet theory, the energy of any decomposition level of all mother wavelet for parameters detection is calculated. From of these signals, the best of them are selected for ANFIS training for islanding detection purpose. Simulation results confirm the performance of the proposed detection algorithm in comparison with existing methods

  19. Auto-adaptative Robot-aided Therapy based in 3D Virtual Tasks controlled by a Supervised and Dynamic Neuro-Fuzzy System

    Directory of Open Access Journals (Sweden)

    Luis Daniel Lledó

    2015-03-01

    Full Text Available This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise.

  20. A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand

    Science.gov (United States)

    Noori, Roohollah; Safavi, Salman; Nateghi Shahrokni, Seyyed Afshin

    2013-07-01

    The five-day biochemical oxygen demand (BOD5) is one of the key parameters in water quality management. In this study, a novel approach, i.e., reduced-order adaptive neuro-fuzzy inference system (ROANFIS) model was developed for rapid estimation of BOD5. In addition, an uncertainty analysis of adaptive neuro-fuzzy inference system (ANFIS) and ROANFIS models was carried out based on Monte-Carlo simulation. Accuracy analysis of ANFIS and ROANFIS models based on both developed discrepancy ratio and threshold statistics revealed that the selected ROANFIS model was superior. Pearson correlation coefficient (R) and root mean square error for the best fitted ROANFIS model were 0.96 and 7.12, respectively. Furthermore, uncertainty analysis of the developed models indicated that the selected ROANFIS had less uncertainty than the ANFIS model and accurately forecasted BOD5 in the Sefidrood River Basin. Besides, the uncertainty analysis also showed that bracketed predictions by 95% confidence bound and d-factor in the testing steps for the selected ROANFIS model were 94% and 0.83, respectively.

  1. A neuro-fuzzy inference system tuned by particle swarm optimization algorithm for sensor monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Mauro Vitor de [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil). Div. de Instrumentacao e Confiabilidade Humana]. E-mail: mvitor@ien.gov.br; Schirru, Roberto [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia. Lab. de Monitoracao de Processos

    2005-07-01

    A neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm has been developed for monitor the relevant sensor in a nuclear plant using the information of other sensors. The antecedent parameters of the ANFIS that estimates the relevant sensor signal are optimized by a PSO algorithm and consequent parameters use a least-squares algorithm. The proposed sensor-monitoring algorithm was demonstrated through the estimation of the nuclear power value in a pressurized water reactor using as input to the ANFIS six other correlated signals. The obtained results are compared to two similar ANFIS using one gradient descendent (GD) and other genetic algorithm (GA), as antecedent parameters training algorithm. (author)

  2. A neuro-fuzzy inference system tuned by particle swarm optimization algorithm for sensor monitoring

    International Nuclear Information System (INIS)

    Oliveira, Mauro Vitor de; Schirru, Roberto

    2005-01-01

    A neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm has been developed for monitor the relevant sensor in a nuclear plant using the information of other sensors. The antecedent parameters of the ANFIS that estimates the relevant sensor signal are optimized by a PSO algorithm and consequent parameters use a least-squares algorithm. The proposed sensor-monitoring algorithm was demonstrated through the estimation of the nuclear power value in a pressurized water reactor using as input to the ANFIS six other correlated signals. The obtained results are compared to two similar ANFIS using one gradient descendent (GD) and other genetic algorithm (GA), as antecedent parameters training algorithm. (author)

  3. EMG signals characterization in three states of contraction by fuzzy network and feature extraction

    CERN Document Server

    Mokhlesabadifarahani, Bita

    2015-01-01

    Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. This brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and modeling purposes. The neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.

  4. Active Head Motion Compensation of TMS Robotic System Using Neuro-Fuzzy Estimation

    Directory of Open Access Journals (Sweden)

    Wan Zakaria W.N.

    2016-01-01

    Full Text Available Transcranial Magnetic Stimulation (TMS allows neuroscientist to study human brain behaviour and also become an important technique for changing the activity of brain neurons and the functions they sub serve. However, conventional manual procedure and robotized TMS are currently unable to precisely position the TMS coil because of unconstrained subject’s head movement and excessive contact force between the coil and subject’s head. This paper addressed this challenge by proposing an adaptive neuro-fuzzy force control to enable low contact force with a moving target surface. A learning and adaption mechanism is included in the control scheme to improve position disturbance estimation. The results show the ability of the proposed force control scheme to compensate subject’s head motions while maintaining desired contact force, thus allowing for more accurate and repeatable TMS procedures.

  5. Proposta de um modelo de maturidade para sítios de governo eletrônico

    Directory of Open Access Journals (Sweden)

    Rafael de Mello Lechakoski

    2015-09-01

    Full Text Available Introdução: Tendo em vista a crescente utilização da tecnologia da informação - em especial a internet - pelo governo como meio de interação com cidadãos, a investigação apresenta estudo bibliográfico acerca de modelos de maturidade para governo eletrônico, abordando os temas de serviços públicos e de maturidade tanto para conceituar a Maturidade em Governo Eletrônico como para propor um modelo de maturidade para sítios de governo eletrônico. Método: Para atendimento ao propósito da pesquisa efetuou-se coleta intencional de 24 modelos de maturidade para governo eletrônico e se procedeu a análise comparativa entre eles verificando-se suas limitações em relação aos serviços prestados; à acessibilidade; à usabilidade e à integração com redes sociais, considerando-se a realidade brasileira e os requisitos previstos no Modelo de Acessibilidade em Governo Eletrônico (e-MAG. Resultados: Propõe-se um modelo de maturidade para sítios de governo eletrônico baseado na convergência dos modelos analisados, o qual é composto por quatro estágios voltados à avaliação de requisitos de serviços, acessibilidade, usabilidade e redes sociais. Diferentemente dos demais analisados, o modelo apresentado considera tanto as recomendações de acessibilidade para internet do World Wide Web Consortium (W3C, como aquelas de acessibilidade e usabilidade do governo brasileiro (e-MAG. Em cada estágio proposto apresentam-se exemplos de requisitos que podem ser avaliados e, potencialmente, transformados em indicadores. Conclusão: A utilização de um modelo para avaliar a maturidade em sítios de governo eletrônico evidencia a situação em que o sítio se encontra em relação ao que é esperado em cada estágio e qual é a linha conceitual de evolução dentro do modelo de maturidade, que reflete em melhorias a seus usuários ou cidadãos de maneira geral.

  6. New neuro-fuzzy system-based holey polymer fibers drawing process

    Science.gov (United States)

    Mohammed Salim, Omar Nameer

    2017-10-01

    Furnace temperature (T), draw tension (TE), and draw ratio (Dr) are the main parameters that could directly affect holey polymer fiber (HPF) production during the drawing stage. Therefore, a suitable mechanism to control (T), (TE), and (Dr) is required to enhance the HPF production process. The conventional approaches, such as observation and tuning technique, experience many difficulties in realizing the accurate values of (T), (TE), and (Dr) in addition to being expensive and time consuming. Therefore, an artificial intelligence model using the adaptive neuro-fuzzy system (ANFIS) method is proposed as an effective solution to achieve an accurate value of the main parameters that affect HPF drawing. Three ANFIS models are developed and tested to determine which one has the best performance for emulating the operation of HPF drawing tower. The ANFIS model with a gbell MF provides a better performance than Gaussian MF ANFIS model and triangular MF ANFIS model in terms of lower mean absolute error and mean square error. Furthermore, the proposed gbell MF model achieved the highest Q-Q response, which indicates the excellent performance of this model.

  7. Um modelo para a avaliação da eficiência hídrica de produtos

    OpenAIRE

    Rodrigues, Carla Andreia Pimentel

    2008-01-01

    No presente trabalho é proposto um modelo de Certificação e Rotulagem da Eficiência Hídrica de Produtos. É ainda feita uma descrição de outros sistemas análogos, actualmente implementados em diversos países, comparando os respectivos modelos, e é analisado o caso particular do sistema de rotulagem que se pretende implementar em Portugal, para certificação de eficiência hídrica de produtos. Finalmente, apresenta-se um caso de estudo para avaliação da poupança de água co...

  8. Preliminary Test of Adaptive Neuro-Fuzzy Inference System Controller for Spacecraft Attitude Control

    Directory of Open Access Journals (Sweden)

    Sung-Woo Kim

    2012-12-01

    Full Text Available The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS. An ANFIS produces a control signal for one of the three axes of a spacecraft’s body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

  9. Nonlinear aeroacoustic characterization of Helmholtz resonators with a local-linear neuro-fuzzy network model

    Science.gov (United States)

    Förner, K.; Polifke, W.

    2017-10-01

    The nonlinear acoustic behavior of Helmholtz resonators is characterized by a data-based reduced-order model, which is obtained by a combination of high-resolution CFD simulation and system identification. It is shown that even in the nonlinear regime, a linear model is capable of describing the reflection behavior at a particular amplitude with quantitative accuracy. This observation motivates to choose a local-linear model structure for this study, which consists of a network of parallel linear submodels. A so-called fuzzy-neuron layer distributes the input signal over the linear submodels, depending on the root mean square of the particle velocity at the resonator surface. The resulting model structure is referred to as an local-linear neuro-fuzzy network. System identification techniques are used to estimate the free parameters of this model from training data. The training data are generated by CFD simulations of the resonator, with persistent acoustic excitation over a wide range of frequencies and sound pressure levels. The estimated nonlinear, reduced-order models show good agreement with CFD and experimental data over a wide range of amplitudes for several test cases.

  10. Using an adaptive fuzzy-logic system to optimize the performances and the reduction of chattering phenomenon in the control of induction motor

    Directory of Open Access Journals (Sweden)

    Barazane Linda

    2009-01-01

    Full Text Available Neural networks and fuzzy inference systems are becoming well recognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance. Also, by combining these two features, more versatile and robust models, called 'neuro-fuzzy' architectures have been developed. The motivation behind the use of neuro-fuzzy approaches is based on the complexity of real life systems, ambiguities on sensory information or time-varying nature of the system under investigation. In this way, the present contribution concerns the application of neuro-fuzzy approach in order to perform the responses of the speed regulation and to reduce the chattering phenomenon introduced by sliding mode control, which is very harmful to the actuators in our case and may excite the unmodeled dynamics of the system. The type of the neuro-fuzzy system used here is called:' adaptive neuro fuzzy inference controller (ANFIS'. This neuro-fuzzy is destined to replace the speed fuzzy sliding mode controller after its training process. Simulation results reveal some very interesting features. .

  11. Um modelo integrado para a programação de voos e alocação de frotas

    Directory of Open Access Journals (Sweden)

    Daniel Jorge Caetano

    2011-11-01

    Full Text Available Este artigo apresenta um modelo para a definição (otimização da malha a ser atendida por uma empresa aérea, resolvendo, de forma integrada, os problemas de programação de voos e alocação de frotas. O modelo inclui restrições operacionais específicas como a existência de slots de pouso e decolagem e a limitada flexibilidade de opções de aeroportos oriunda de um tráfego composto primariamente por passageiros. O modelo foi testado e aplicado com sucesso a um caso de uma empresa aérea regional brasileira, resultando em uma programação de voos completa e provendo informações de suporte à decisão sobre a possibilidade de novos voos ou o uso de outros tipos de aeronave.

  12. Approaching bathymetry estimation from high resolution multispectral satellite images using a neuro-fuzzy technique

    Science.gov (United States)

    Corucci, Linda; Masini, Andrea; Cococcioni, Marco

    2011-01-01

    This paper addresses bathymetry estimation from high resolution multispectral satellite images by proposing an accurate supervised method, based on a neuro-fuzzy approach. The method is applied to two Quickbird images of the same area, acquired in different years and meteorological conditions, and is validated using truth data. Performance is studied in different realistic situations of in situ data availability. The method allows to achieve a mean standard deviation of 36.7 cm for estimated water depths in the range [-18, -1] m. When only data collected along a closed path are used as a training set, a mean STD of 45 cm is obtained. The effect of both meteorological conditions and training set size reduction on the overall performance is also investigated.

  13. Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems

    Energy Technology Data Exchange (ETDEWEB)

    Jassar, S.; Zhao, L. [Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON (Canada); Liao, Z. [Department of Architectural Science, Ryerson University (Canada)

    2009-08-15

    The heating systems are conventionally controlled by open-loop control systems because of the absence of practical methods for estimating average air temperature in the built environment. An inferential sensor model, based on adaptive neuro-fuzzy inference system modeling, for estimating the average air temperature in multi-zone space heating systems is developed. This modeling technique has the advantage of expert knowledge of fuzzy inference systems (FISs) and learning capability of artificial neural networks (ANNs). A hybrid learning algorithm, which combines the least-square method and the back-propagation algorithm, is used to identify the parameters of the network. This paper describes an adaptive network based inferential sensor that can be used to design closed-loop control for space heating systems. The research aims to improve the overall performance of heating systems, in terms of energy efficiency and thermal comfort. The average air temperature results estimated by using the developed model are strongly in agreement with the experimental results. (author)

  14. Um modelo coerente de gerenciamento de risco de liquidez para o contexto brasileiro

    OpenAIRE

    Mauro Mastella

    2008-01-01

    O objetivo desta dissertação é desenvolver um modelo de gestão de risco de liquidez que flexibilize as principais simplificações geralmente realizadas pelas instituições financeiras na aplicação de testes de estresse para gerenciamento do risco de liquidez. Assim, esta pesquisa consiste em estimar um fluxo de caixa unificado de uma instituição sob diferentes cenários econômicos, testando se as volatilidades implícitas das opções são um bom indicador de mudanças significativas no mercado de ca...

  15. DESIGN COM CRIANÇAS: DA PRÁTICA A UM MODELO DE PROCESSO

    OpenAIRE

    Amanda Meincke Melo; M. Cecília C. Baranauskas; Sílvia Cristina de Matos Soares

    2009-01-01

    Modelos, métodos e processos de design em geral têm considerado o sujeito como “usuário” de tecnologia criada para ele a partir de um modelo conceitual de “quem” é e “de que necessita” esse sujeito. Se essa é a realidade no mundo do adulto, mais ainda no caso de tecnologia construída para crianças. Nossa experiência em projetos de pesquisa envolvendo o design de ambientes educacionais com crianças tem mostrado oportunidades e limitações associadas ao design de tecnologia com o envolvimento de...

  16. Um modelo da maturidade da gestão da demanda: Um estudo multicaso na cadeia de suprimento de produtos de mercearia básica

    Directory of Open Access Journals (Sweden)

    Daniela de Castro Melo

    2015-03-01

    Full Text Available A gestão da demanda deve equilibrar as necessidades dos clientes com as capacidades da empresa, reduzindo incertezas, resultando em fluxos mais eficientes na cadeia de suprimentos. O objetivo deste estudo foi propor um modelo para analisar a maturidade do processo de gestão da demanda na cadeia de suprimentos de produtos de mercearia básica. Espera-se que o modelo resultante possa subsidiar as empresas a se estruturarem e desenvolverem competências para atingir maturidade no processo de gestão da demanda. Para tal, realizou-se um estudo multicaso que envolveu 22 empresas, dentre as quais: indústrias fornecedoras de produtos de mercearia básica, atacadistas distribuidores e supermercados. Foram realizadas observações diretas e 33 entrevistas nestas empresas. A pesquisa possibilitou estruturar um modelo de maturidade em gestão da demanda composto de quatro estágios divididos em nove dimensões: compartilhamento de informações; planejamento de ações conjuntas; execuções de ações conjuntas; interações inter e intraempresas; indicadores de desempenho; envolvimento da alta gerência; segmentação de clientes; segmentação de fornecedores; tecnologia da informação. Este modelo pode permitir às empresas identificar em qual estágio se encontram e em qual dimensão precisam focar seus esforços. Cada uma das dimensões representa um meio para que as empresas possam se estruturar e otimizar a rentabilidade por meio da melhoria da eficiência e da eficácia na cadeia de suprimentos.

  17. Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)

    International Nuclear Information System (INIS)

    Kakar, Manish; Nystroem, Haakan; Aarup, Lasse Rye; Noettrup, Trine Jakobi; Olsen, Dag Rune

    2005-01-01

    The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude

  18. Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)

    Energy Technology Data Exchange (ETDEWEB)

    Kakar, Manish [Department of Radiation Biology, Norwegian Radium Hospital, Montebello, 0310 Oslo (Norway); Nystroem, Haakan [Department of Radiation Oncology, The Finsen Centre, Rigshospitalet, Copenhagen (Denmark); Aarup, Lasse Rye [Department of Radiation Oncology, The Finsen Centre, Rigshospitalet, Copenhagen (Denmark); Noettrup, Trine Jakobi [Department of Radiation Oncology, The Finsen Centre, Rigshospitalet, Copenhagen (Denmark); Olsen, Dag Rune [Department of Radiation Biology, Norwegian Radium Hospital, Montebello, 0310 Oslo (Norway); Department of Medical Physics and Technology, Norwegian Radium Hospital, Oslo (Norway); Department of Physics, University of Oslo (Norway)

    2005-10-07

    The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude.

  19. Condition monitoring with wind turbine SCADA data using Neuro-Fuzzy normal behavior models

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2012-01-01

    System (ANFIS) models are employed to learn the normal behavior in a training phase, where the component condition can be considered healthy. In the application phase the trained models are applied to predict the target signals, e.g. temperatures, pressures, currents, power output, etc. The behavior......This paper presents the latest research results of a project that focuses on normal behavior models for condition monitoring of wind turbines and their components, via ordinary Supervisory Control And Data Acquisition (SCADA) data. In this machine learning approach Adaptive Neuro-Fuzzy Interference...... of the prediction error is used as an indicator for normal and abnormal behavior, with respect to the learned behavior. The advantage of this approach is that the prediction error is widely decoupled from the typical fluctuations of the SCADA data caused by the different turbine operational modes. To classify...

  20. A neuro-fuzzy model to predict the inflow to the guardialfiera multipurpose dam (Southern Italy at medium-long time scales

    Directory of Open Access Journals (Sweden)

    L.F. Termite

    2013-09-01

    Full Text Available Intelligent computing tools based on fuzzy logic and artificial neural networks have been successfully applied in various problems with superior performances. A new approach of combining these two powerful tools, known as neuro-fuzzy systems, has increasingly attracted scientists in different fields. Few studies have been undertaken to evaluate their performances in hydrologic modeling. Specifically are available rainfall-runoff modeling typically at very short time scales (hourly, daily or event for the real-time forecasting of floods with in input precipitation and past runoff (i.e. inflow rate and in few cases models for the prediction of the monthly inflows to a dam using the past inflows as input. This study presents an application of an Adaptive Network-based Fuzzy Inference System (ANFIS, as a neuro-fuzzy-computational technique, in the forecasting of the inflow to the Guardialfiera multipurpose dam (CB, Italy at the weekly and monthly time scale. The latter has been performed both directly at monthly scale (monthly input data and iterating the weekly model. Twenty-nine years of rainfall, temperature, water level in the reservoir and releases to the different uses were available. In all simulations meteorological input data were used and in some cases also the past inflows. The performance of the defined ANFIS models were established by different efficiency and correlation indices. The results at the weekly time scale can be considered good, with a Nash- Sutcliffe efficiency index E = 0.724 in the testing phase. At the monthly time scale, satisfactory results were obtained with the iteration of the weekly model for the prediction of the incoming volume up to 3 weeks ahead (E = 0.574, while the direct simulation of monthly inflows gave barely satisfactory results (E = 0.502. The greatest difficulties encountered in the analysis were related to the reliability of the available data. The results of this study demonstrate the promising

  1. Predictive models for PEM-electrolyzer performance using adaptive neuro-fuzzy inference systems

    Energy Technology Data Exchange (ETDEWEB)

    Becker, Steffen [University of Tasmania, Hobart 7001, Tasmania (Australia); Karri, Vishy [Australian College of Kuwait (Kuwait)

    2010-09-15

    Predictive models were built using neural network based Adaptive Neuro-Fuzzy Inference Systems for hydrogen flow rate, electrolyzer system-efficiency and stack-efficiency respectively. A comprehensive experimental database forms the foundation for the predictive models. It is argued that, due to the high costs associated with the hydrogen measuring equipment; these reliable predictive models can be implemented as virtual sensors. These models can also be used on-line for monitoring and safety of hydrogen equipment. The quantitative accuracy of the predictive models is appraised using statistical techniques. These mathematical models are found to be reliable predictive tools with an excellent accuracy of {+-}3% compared with experimental values. The predictive nature of these models did not show any significant bias to either over prediction or under prediction. These predictive models, built on a sound mathematical and quantitative basis, can be seen as a step towards establishing hydrogen performance prediction models as generic virtual sensors for wider safety and monitoring applications. (author)

  2. Adaptive Neuro-Fuzzy Inference System Models for Force Prediction of a Mechatronic Flexible Structure

    DEFF Research Database (Denmark)

    Achiche, S.; Shlechtingen, M.; Raison, M.

    2016-01-01

    This paper presents the results obtained from a research work investigating the performance of different Adaptive Neuro-Fuzzy Inference System (ANFIS) models developed to predict excitation forces on a dynamically loaded flexible structure. For this purpose, a flexible structure is equipped...... obtained from applying a random excitation force on the flexible structure. The performance of the developed models is evaluated by analyzing the prediction capabilities based on a normalized prediction error. The frequency domain is considered to analyze the similarity of the frequencies in the predicted...... of the sampling frequency and sensor location on the model performance is investigated. The results obtained in this paper show that ANFIS models can be used to set up reliable force predictors for dynamical loaded flexible structures, when a certain degree of inaccuracy is accepted. Furthermore, the comparison...

  3. Um modelo integrado econométrico+insumo-produto para previsão de longo prazo da demanda de combustíveis no Brasil

    Directory of Open Access Journals (Sweden)

    Flaviane Souza Santiago

    2012-05-01

    Full Text Available O artigo apresenta um modelo integrado de tipo econométrico+insumo-produto para previsões de longo prazo da demanda de combustíveis no Brasil. O modelo é baseado na integração por ligação de um modelo vetorial de correção de erros com um modelo de insumo-produto híbrido para a economia brasileira e permite fazer previsões anuais de consumo para quatro grupos de combustível: gasolina, óleo diesel, óleo combustível e álcool. No processo de desenvolvimento, tanto o modelo econométrico quanto o modelo integrado foram submetidos a testes de desempenho preditivo, com o último sendo calibrado para melhor performance, usando-se dados disponíveis para o período de 2004 a 2007. Posteriormente, o modelo integrado é usado para gerar previsões no período de 2008 a 2017. As previsões são baseadas em dois cenários alternativos, um prevendo duração curta, e o outro, duração longa para a atual crise econômica mundial. Os resultados obtidos indicam que, em ambos os casos, ocorrerá significativo aumento da demanda de combustíveis nos próximos 10 anos.

  4. Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants

    Directory of Open Access Journals (Sweden)

    Yea-Kuang Chan

    2012-01-01

    Full Text Available Due to the very complex sets of component systems, interrelated thermodynamic processes and seasonal change in operating conditions, it is relatively difficult to find an accurate model for turbine cycle of nuclear power plants (NPPs. This paper deals with the modeling of turbine cycles to predict turbine-generator output using an adaptive neuro-fuzzy inference system (ANFIS for Unit 1 of the Kuosheng NPP in Taiwan. Plant operation data obtained from Kuosheng NPP between 2006 and 2011 were verified using a linear regression model with a 95% confidence interval. The key parameters of turbine cycle, including turbine throttle pressure, condenser backpressure, feedwater flow rate and final feedwater temperature are selected as inputs for the ANFIS based turbine cycle model. In addition, a thermodynamic turbine cycle model was developed using the commercial software PEPSE® to compare the performance of the ANFIS based turbine cycle model. The results show that the proposed ANFIS based turbine cycle model is capable of accurately estimating turbine-generator output and providing more reliable results than the PEPSE® based turbine cycle models. Moreover, test results show that the ANFIS performed better than the artificial neural network (ANN, which has also being tried to model the turbine cycle. The effectiveness of the proposed neuro-fuzzy based turbine cycle model was demonstrated using the actual operating data of Kuosheng NPP. Furthermore, the results also provide an alternative approach to evaluate the thermal performance of nuclear power plants.

  5. A Combined Methodology of Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm for Short-term Energy Forecasting

    Directory of Open Access Journals (Sweden)

    KAMPOUROPOULOS, K.

    2014-02-01

    Full Text Available This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS and Genetic Algorithms (GA. The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short-term load forecasting for the different modeled consumption processes.

  6. Prediction of ultrasonic pulse velocity for enhanced peat bricks using adaptive neuro-fuzzy methodology.

    Science.gov (United States)

    Motamedi, Shervin; Roy, Chandrabhushan; Shamshirband, Shahaboddin; Hashim, Roslan; Petković, Dalibor; Song, Ki-Il

    2015-08-01

    Ultrasonic pulse velocity is affected by defects in material structure. This study applied soft computing techniques to predict the ultrasonic pulse velocity for various peats and cement content mixtures for several curing periods. First, this investigation constructed a process to simulate the ultrasonic pulse velocity with adaptive neuro-fuzzy inference system. Then, an ANFIS network with neurons was developed. The input and output layers consisted of four and one neurons, respectively. The four inputs were cement, peat, sand content (%) and curing period (days). The simulation results showed efficient performance of the proposed system. The ANFIS and experimental results were compared through the coefficient of determination and root-mean-square error. In conclusion, use of ANFIS network enhances prediction and generation of strength. The simulation results confirmed the effectiveness of the suggested strategies. Copyright © 2015 Elsevier B.V. All rights reserved.

  7. MODELOS DE SISTEMAS MRP CERRADOS INTEGRANDO INCERTIDUMBRE MODELOS DE SISTEMAS MRP FECHADOS INTEGRANDO INCERTEZA CLOSED MODELS OF MRP SYSTEMS CONSIDERING UNCERTAINTIES

    Directory of Open Access Journals (Sweden)

    Martín Dario Arango

    2012-12-01

    Full Text Available En este artículo se muestran cuatro modelos de los sistemas MRP cerrados con incertidumbre en los componentes de producción, como son: la capacidad necesaria de fabricación de cada producto, el tiempo de entrega y la disponibilidad del inventario. Dichos parámetros se tratan mediante la lógica difusa modelizando un sistema MRP cerrado determinista. Por tanto, se presentan inicialmente tres modelos de sistema MRP cerrado, donde cada uno considera de forma independiente la incertidumbre en capacidad, tiempo de entrega y disponibilidad de inventario. Igualmente, se presenta un cuarto modelo de sistema MRP cerrado que de forma conjunta analiza la incertidumbre en los tres parámetros mencionados. Cada uno de estos modelos es validado con información de una empresa del sector eléctrico colombiano, evaluando el costo total del plan de producción, nivel de inventarios, nivel de servicio y complejidad computacional.Neste artigo mostram-se quatro modelos dos sistemas MRP fechados com incerteza nos componentes de produção, como são: a capacidade necessária de fabricação da cada produto, o tempo de entrega e a disponibilidade do inventario. Ditos parâmetros tratam-se mediante a lógica difusa modelando um sistema MRP fechado determinista. Por tanto, apresentam-se inicialmente três modelos de sistema MRP fechado, onde a cada um considera de forma independente a incerteza em capacidade, tempo de entrega e disponibilidade de inventario. Igualmente, apresenta-se um quarto modelo de sistema MRP fechado que de forma conjunta analisa a incerteza nos três parâmetros mencionados. A cada um destes modelos é validado com informação de uma empresa do setor elétrico colombiano, avaliando o custo total do plano de produção, nível de estoques, nível de serviço e complexidade computacional.In this paper, we present four models of uncertainty in the MRP closed systems in the production components, such as: manufacturing capacity of each product

  8. Um novo modelo de previsão de demanda para inovações radicais

    Directory of Open Access Journals (Sweden)

    Donald Neumann

    2014-09-01

    Full Text Available Este trabalho apresenta um novo modelo para a previsão de demanda de inovações radicais baseada em simulação de Dinâmica de Sistemas que combina conceitos do modelo de difusão de Bass e do modelo de escolha discreta. Diferentemente de outras abordagens existentes, esta proposta permite estimar não somente a fatia de mercado do produto, mas também seu comportamento no tempo (timing, a partir das preferências individuais do consumidor e das forças que as influenciam. O modelo proposto pode ser facilmente parametrizado através da Conjoint Analysis e foi testado em escala real no mercado alemão de carros elétricos. Os resultados obtidos colocam em evidência o potencial da abordagem proposta, auxiliando na compreensão dos principais fatores na escolha desse produto.

  9. Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Saleh Shahinfar

    2012-01-01

    Full Text Available Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.

  10. Wavelet decomposition and neuro-fuzzy hybrid system applied to short-term wind power

    Energy Technology Data Exchange (ETDEWEB)

    Fernandez-Jimenez, L.A.; Mendoza-Villena, M. [La Rioja Univ., Logrono (Spain). Dept. of Electrical Engineering; Ramirez-Rosado, I.J.; Abebe, B. [Zaragoza Univ., Zaragoza (Spain). Dept. of Electrical Engineering

    2010-03-09

    Wind energy has become increasingly popular as a renewable energy source. However, the integration of wind farms in the electrical power systems presents several problems, including the chaotic fluctuation of wind flow which results in highly varied power generation from a wind farm. An accurate forecast of wind power generation has important consequences in the economic operation of the integrated power system. This paper presented a new statistical short-term wind power forecasting model based on wavelet decomposition and neuro-fuzzy systems optimized with a genetic algorithm. The paper discussed wavelet decomposition; the proposed wind power forecasting model; and computer results. The original time series, the mean electric power generated in a wind farm, was decomposing into wavelet coefficients that were utilized as inputs for the forecasting model. The forecasting results obtained with the final models were compared to those obtained with traditional forecasting models showing a better performance for all the forecasting horizons. 13 refs., 1 tab., 4 figs.

  11. Um modelo de simulação para gestão da capacidade dos aeroportos brasileiros

    Directory of Open Access Journals (Sweden)

    Antonio Rodolfo Araujo Marcos

    Full Text Available A importância do setor aéreo para o desenvolvimento de um país e o crescimento da demanda aérea brasileira implica na necessidade de uma gestão eficiente dos aeroportos, buscando o equilíbrio entre a capacidade e a demanda aérea. Diante disso, este artigo apresenta um modelo em Dinâmica de Sistemas capaz de auxiliar a gestão aeroportuária brasileira em relação ao dimensionamento dos subsistemas de um aeroporto (Terminal de Passageiros, Pistas e Pátio. O modelo utiliza dados sobre demanda aérea de passageiros, número médio de passageiros por voo, demanda hora-pico, nível de serviço do terminal de passageiros e tempo médio de estacionamento das aeronaves para analisar a situação de treze aeroportos brasileiros. Por fim, através da comparação da capacidade ideal produzida pelo modelo de simulação com a capacidade real instalada, o artigo classifica a situação de cada um dos subsistemas dos aeroportos estudados em adequada, crítica e preocupante. Entre os principais resultados obtidos pode-se destacar que: a Pista é o subsistema que apresenta melhores condições, enquanto que o terminal de passageiros é o subsistema em pior condição; os investimentos e aumento da capacidade são insuficientes para atender uma demanda de crescimento de 5% ao ano na hora-pico.

  12. Static security-based available transfer capability using adaptive neuro fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Venkaiah, C.; Vinod Kumar, D.M.

    2010-07-01

    In a deregulated power system, power transactions between a seller and a buyer can only be scheduled when there is sufficient available transfer capability (ATC). Internet-based, open access same-time information systems (OASIS) provide market participants with ATC information that is continuously updated in real time. Static security-based ATC can be computed for the base case system as well as for the critical line outages of the system. Since critical line outages are based on static security analysis, the computation of static security based ATC using conventional methods is both tedious and time consuming. In this study, static security-based ATC was computed for real-time applications using 3 artificial intelligent methods notably the back propagation algorithm (BPA), the radial basis function (RBF) neural network, and the adaptive neuro fuzzy inference system (ANFIS). An IEEE 24-bus reliability test system (RTS) and 75-bus practical system were used to test these 3 different intelligent methods. The results were compared with the conventional full alternating current (AC) load flow method for different transactions.

  13. Static security-based available transfer capability using adaptive neuro fuzzy inference system

    International Nuclear Information System (INIS)

    Venkaiah, C.; Vinod Kumar, D.M.

    2010-01-01

    In a deregulated power system, power transactions between a seller and a buyer can only be scheduled when there is sufficient available transfer capability (ATC). Internet-based, open access same-time information systems (OASIS) provide market participants with ATC information that is continuously updated in real time. Static security-based ATC can be computed for the base case system as well as for the critical line outages of the system. Since critical line outages are based on static security analysis, the computation of static security based ATC using conventional methods is both tedious and time consuming. In this study, static security-based ATC was computed for real-time applications using 3 artificial intelligent methods notably the back propagation algorithm (BPA), the radial basis function (RBF) neural network, and the adaptive neuro fuzzy inference system (ANFIS). An IEEE 24-bus reliability test system (RTS) and 75-bus practical system were used to test these 3 different intelligent methods. The results were compared with the conventional full alternating current (AC) load flow method for different transactions.

  14. Prediction of mechanical properties of a warm compacted molybdenum prealloy using artificial neural network and adaptive neuro-fuzzy models

    International Nuclear Information System (INIS)

    Zare, Mansour; Vahdati Khaki, Jalil

    2012-01-01

    Highlights: ► ANNs and ANFIS fairly predicted UTS and YS of warm compacted molybdenum prealloy. ► Effects of composition, temperature, compaction pressure on output were studied. ► ANFIS model was in better agreement with experimental data from published article. ► Sintering temperature had the most significant effect on UTS and YS. -- Abstract: Predictive models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were successfully developed to predict yield strength and ultimate tensile strength of warm compacted 0.85 wt.% molybdenum prealloy samples. To construct these models, 48 different experimental data were gathered from the literature. A portion of the data set was randomly chosen to train both ANN with back propagation (BP) learning algorithm and ANFIS model with Gaussian membership function and the rest was implemented to verify the performance of the trained network against the unseen data. The generalization capability of the networks was also evaluated by applying new input data within the domain covered by the training pattern. To compare the obtained results, coefficient of determination (R 2 ), root mean squared error (RMSE) and average absolute error (AAE) indexes were chosen and calculated for both of the models. The results showed that artificial neural network and adaptive neuro-fuzzy system were both potentially strong for prediction of the mechanical properties of warm compacted 0.85 wt.% molybdenum prealloy; however, the proposed ANFIS showed better performance than the ANN model. Also, the ANFIS model was subjected to a sensitivity analysis to find the significant inputs affecting mechanical properties of the samples.

  15. Evaluation of Regression and Neuro_Fuzzy Models in Estimating Saturated Hydraulic Conductivity

    Directory of Open Access Journals (Sweden)

    J. Behmanesh

    2015-06-01

    Full Text Available Study of soil hydraulic properties such as saturated and unsaturated hydraulic conductivity is required in the environmental investigations. Despite numerous research, measuring saturated hydraulic conductivity using by direct methods are still costly, time consuming and professional. Therefore estimating saturated hydraulic conductivity using rapid and low cost methods such as pedo-transfer functions with acceptable accuracy was developed. The purpose of this research was to compare and evaluate 11 pedo-transfer functions and Adaptive Neuro-Fuzzy Inference System (ANFIS to estimate saturated hydraulic conductivity of soil. In this direct, saturated hydraulic conductivity and physical properties in 40 points of Urmia were calculated. The soil excavated was used in the lab to determine its easily accessible parameters. The results showed that among existing models, Aimrun et al model had the best estimation for soil saturated hydraulic conductivity. For mentioned model, the Root Mean Square Error and Mean Absolute Error parameters were 0.174 and 0.028 m/day respectively. The results of the present research, emphasises the importance of effective porosity application as an important accessible parameter in accuracy of pedo-transfer functions. sand and silt percent, bulk density and soil particle density were selected to apply in 561 ANFIS models. In training phase of best ANFIS model, the R2 and RMSE were calculated 1 and 1.2×10-7 respectively. These amounts in the test phase were 0.98 and 0.0006 respectively. Comparison of regression and ANFIS models showed that the ANFIS model had better results than regression functions. Also Nuro-Fuzzy Inference System had capability to estimatae with high accuracy in various soil textures.

  16. Fuzzy ABC: Modelando a Incerteza na Alocação dos Custos AmbientaisFuzzy ABC: Modeling the Uncertainty in Environmental Cost AllocationFuzzy ABC: Modelando la Incertidumbre en la Alocation de los Costos Ambientales

    Directory of Open Access Journals (Sweden)

    BORBA, José Alonso

    2007-05-01

    Full Text Available RESUMOEm muitos casos, prevenir a poluição e a destruição do meio ambiente é menos oneroso do que remediar esses danos. Nesse contexto, a alocação de custos ambientais aos produtos permite uma melhor visualização e análise da rentabilidade dos produtos. Entretanto, a alocação dos custos ambientais aos produtos envolve informações estimadas e assume uma linearidade entre o consumo das atividades e os produtos, que muitas vezes não existe. Para contemplar essa não linearidade, esta pesquisa apresenta uma metodologia baseada na utilização da lógica fuzzy para modelar a incerteza e a subjetividade, inerentes ao processo de alocação dos custos ambientais. Para isso, além de um estudo de caso desenvolvido por Hansen e Mowen (2001, p. 584, que foi utilizado como referência, outras variáveis foram incorporadas. Em seguida, uma proposta de solução, que utiliza fundamentos da teoria dos conjuntos fuzzy, ou nebulosos, foi desenvolvida com o objetivo de contemplar a subjetividade e a incerteza na alocação dos custos ambientais. Para simular esse modelo, foram estabelecidas 126 regras de inferência. A etapa final da elaboração do modelo nebuloso consistiu na fuzzificação e defuzzificação dos dados existentes e dos novos direcionadores gerados por intermédio da utilização do software FuzzyTECH®. Os resultados encontrados no modelo proposto - FuzzyABC (Fuzzy Activity Based Costing - evidenciam que a lógica fuzzy pode ser utilizada como uma importante ferramenta para tratar da ambigüidade e da incerteza, inerentes ao processo de alocação dos custos ambientais.ABSTRACTIn many cases, preventing pollution and environmental destruction is cheaper than remedying these damages. In this sense, environmental cost allocation enables a better visualization and analysis of a product’s profitability. However, the environmental allocation process involves estimated information and assumes linearity between activity consumption

  17. Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources

    Directory of Open Access Journals (Sweden)

    Otilia Elena Dragomir

    2015-11-01

    Full Text Available The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users’ decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1 and the shape of membership functions (Scenario 2.

  18. Um modelo orientativo para a gestão municipal dos RCCs

    Directory of Open Access Journals (Sweden)

    Maria da Paz Medeiros Fernandes

    Full Text Available Resumo Os resíduos da construção civil (RCCs decorrem de perdas e desperdícios na construção, demolição, reforma e reparos de obras. Sua geração é elevada nos sistemas de produção correntes, organizados em linha com geração e descarte dos resíduos. Tentando minorar essa problemática, municipalidades brasileiras vêm experimentando a gestão diferenciada dos RCCs, notadamente após a Resolução n. 307/2002 (modificada pelas Resoluções ns. 348/2004, 431/2011 e 448/2012 do Conselho Nacional do Meio Ambiente (Conama e a Lei n. 12.305/2010, que instituiu a Política Nacional dos Resíduos Sólidos (PNRS. Com o objetivo de auxiliá-las, o presente artigo, fruto de pesquisa de doutorado, apresenta um modelo orientativo para gestão municipal dos RCCs. Para a elaboração do modelo foi utilizada a metodologia dos sistemas flexíveis ou metodologia SSM (Soft Systems Methodology, adequada para situações reais e complexas como esta, que necessita reunir e sistematizar práticas, legislação, percepções, conceitos e interesses diversos dos atores envolvidos. O levantamento de dados foi realizado de 2009 a 2013 usando pesquisa bibliográfica, documentação indireta em oito municípios com boas práticas e documentação direta em Belo Horizonte e João Pessoa. Como resultado obteve-se um modelo cíclico que, através de suas diretrizes, estratégias e metas, fornece ajuda aos municípios brasileiros na incorporação de melhorias graduais e consistentes para esse desafio.

  19. Prediction of radical scavenging activities of anthocyanins applying adaptive neuro-fuzzy inference system (ANFIS) with quantum chemical descriptors.

    Science.gov (United States)

    Jhin, Changho; Hwang, Keum Taek

    2014-08-22

    Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively.

  20. Prediction of Radical Scavenging Activities of Anthocyanins Applying Adaptive Neuro-Fuzzy Inference System (ANFIS with Quantum Chemical Descriptors

    Directory of Open Access Journals (Sweden)

    Changho Jhin

    2014-08-01

    Full Text Available Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A and electronegativity (χ of flavylium cation, and ionization potential (I of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively.

  1. Um modelo de otimização para alíquotas do IPTU socialmente mais justas

    Directory of Open Access Journals (Sweden)

    José Delfino Sá

    2013-02-01

    Full Text Available Este artigo apresenta um modelo de otimização matemática não linear que determina novas alíquotas para o Imposto sobre a Propriedade Predial e Territorial Urbana (IPTU incidente sobre os apartamentos residenciais no município do Salvador (BA. São considerados a progressividade das alíquotas, os valores venais dos imóveis, as rendas médias dos contribuintes e as usuais metodologias de cálculo do valor do imposto. Os resultados obtidos na aplicação desse modelo demonstram ser possível tratar de forma objetiva e socialmente mais justa a definição das alíquotas de IPTU para todos os tipos de imóveis de um município.

  2. Proposta de um modelo de referência para governo eletrônico 2.0

    OpenAIRE

    Dziekaniak, Gisele Vasconcelos

    2011-01-01

    Propõe-se um modelo de referência para governo eletrônico (e-gov) fundamentado nas prerrogativas da Web 2.0. Este modelo visa contribuir para o desenvolvimento de governos 2.0, os quais são baseados no compartilhamento do conhecimento, bem como na gestão e criação de conteúdos por coletivos e no fortalecimento das relações governo e sociedade. Utiliza-se a pesquisa bibliográfica, assim como a observação direta de redes sociais que possuem como objeto de discussão as plataformas governam...

  3. Um modelo híbrido (CLP-MILP para scheduling de operações em polidutos

    Directory of Open Access Journals (Sweden)

    Leandro Magatão

    2008-12-01

    Full Text Available A eficácia na transferência de derivados de petróleo através de dutos motiva a execução deste trabalho. O objetivo principal é a modelagem do scheduling de um poliduto, isto é, um sistema de dutos que transporta diferentes derivados de petróleo. O poliduto em estudo com 93,5 km de extensão conecta uma refinaria a um terminal portuário. Foi desenvolvido um modelo de otimização baseado na união de Constraint Logic Programming (CLP e Mixed Integer Linear Programming (MILP. O modelo utiliza uma abordagem de decomposição do problema, com representação temporal contínua e calcula janelas de tempo (restrições temporais que devem ser respeitadas. A abordagem híbrida CLP-MILP proporcionou a solução de cenários reais em tempo computacional da ordem de segundos. A resolução computacional do modelo proposto evidenciou novos pontos de operação para o poliduto, proporcionando ganhos operacionais significativos. O modelo implementado configura uma ferramenta de auxílio para tomada de decisões operacionais no cenário estudado.This work is motivated by the need of optimization in the pipeline-oil distribution scenario. The considered problem involves the short-term scheduling of activities in a specific pipeline. The pipeline is 93.5 km in length, and it connects refinery and harbor tankfarms, conveying different types of commodities (gasoline, diesel, kerosene, etc. An optimization model was developed to determine the pipeline scheduling with improved efficiency. Such model combines Constraint Logic Programming (CLP and Mixed Integer Linear Programming (MILP in an integrated CLP-MILP framework. The proposed model uses decomposition strategies, continuous time representation, and intervals that represent time constraints (time windows. Real cases were solved in a reduced computational time (order of seconds. The computational results have demonstrated that the model is able to define new operational points to the pipeline

  4. Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO

    Directory of Open Access Journals (Sweden)

    Juan Hernandez-Vicen

    2018-03-01

    Full Text Available New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator from the University Carlos III of Madrid.

  5. Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.

    Science.gov (United States)

    Shamshirband, Shahaboddin; Petković, Dalibor; Hashim, Roslan; Motamedi, Shervin

    2014-01-01

    Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

  6. An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

    Science.gov (United States)

    Ajay Kumar, M.; Srikanth, N. V.

    2014-03-01

    In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

  7. Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier

    Directory of Open Access Journals (Sweden)

    GEMAN, O.

    2014-02-01

    Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.

  8. Cheap diagnosis using structural modelling and fuzzy-logic based detection

    DEFF Research Database (Denmark)

    Izadi-Zamanabadi, Roozbeh; Blanke, Mogens; Katebi, Serajeddin

    2003-01-01

    relations for linear or non-linear dynamic behaviour, and combine this with fuzzy output observer design to provide an effective diagnostic approach. An adaptive neuro-fuzzy inference method is used. A fuzzy adaptive threshold is employed to cope with practical uncertainty. The methods are demonstrated...... using measurements on a ship propulsion system subject to simulated faults....

  9. Perspectivas para um novo modelo de organização do trabalho da enfermagem

    Directory of Open Access Journals (Sweden)

    Francine Lima Gelbcke

    2004-04-01

    Full Text Available Neste estudo, fundamentado no materialismo histórico e dialético, busco refletir sobre estratégias de re-organização do trabalho da enfermagem, no sentido de minimizar o processo de desgaste físico e psíquico dos trabalhadores. A apreensão dos dados empíricos foi realizada junto aos trabalhadores de enfermagem de duas instituições de saúde, na região sul do país., sendo utilizada entrevista, observação e análise documental. A partir dos indicativos, apresento um modelo de organização, pautado na democracia das relações, visando a expressão dos trabalhadores enquanto sujeitos multidimensionais, como atores sociais e não meros executores de tarefas delegadas, como hoje estabelece a divisão social e técnica do trabalho. Um modelo que tem por objetivo estabelecer relações interpessoais mais harmônicas e horizontais, buscando compartilhar saberes e fazeres.

  10. Performance analysis of electronic power transformer based on neuro-fuzzy controller.

    Science.gov (United States)

    Acikgoz, Hakan; Kececioglu, O Fatih; Yildiz, Ceyhun; Gani, Ahmet; Sekkeli, Mustafa

    2016-01-01

    In recent years, electronic power transformer (EPT), which is also called solid state transformer, has attracted great interest and has been used in place of the conventional power transformers. These transformers have many important functions as high unity power factor, low harmonic distortion, constant DC bus voltage, regulated output voltage and compensation capability. In this study, proposed EPT structure contains a three-phase pulse width modulation rectifier that converts 800 Vrms AC to 2000 V DC bus at input stage, a dual active bridge converter that provides 400 V DC bus with 5:1 high frequency transformer at isolation stage and a three-phase two level inverter that is used to obtain AC output at output stage. In order to enhance dynamic performance of EPT structure, neuro fuzzy controllers which have durable and nonlinear nature are used in input and isolation stages instead of PI controllers. The main aim of EPT structure with the proposed controller is to improve the stability of power system and to provide faster response against disturbances. Moreover, a number of simulation results are carried out to verify EPT structure designed in MATLAB/Simulink environment and to analyze compensation ability for voltage harmonics, voltage flicker and voltage sag/swell conditions.

  11. Adaptive Neuro-Fuzzy Based Gain Controller for Erbium-Doped Fiber Amplifiers

    Directory of Open Access Journals (Sweden)

    YUCEL, M.

    2017-02-01

    Full Text Available Erbium-doped fiber amplifiers (EDFA must have a flat gain profile which is a very important parameter such as wavelength division multiplexing (WDM and dense WDM (DWDM applications for long-haul optical communication systems and networks. For this reason, it is crucial to hold a stable signal power per optical channel. For the purpose of overcoming performance decline of optical networks and long-haul optical systems, the gain of the EDFA must be controlled for it to be fixed at a high speed. In this study, due to the signal power attenuation in long-haul fiber optic communication systems and non-equal signal amplification in each channel, an automatic gain controller (AGC is designed based on the adaptive neuro-fuzzy inference system (ANFIS for EDFAs. The intelligent gain controller is implemented and the performance of this new electronic control method is demonstrated. The proposed ANFIS-based AGC-EDFA uses the experimental dataset to produce the ANFIS-based sets and the rule base. Laser diode currents are predicted within the accuracy rating over 98 percent with the proposed ANFIS-based system. Upon comparing ANFIS-based AGC-EDFA and experimental results, they were found to be very close and compatible.

  12. Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning

    Science.gov (United States)

    Talei, Amin; Chua, Lloyd Hock Chye; Quek, Chai; Jansson, Per-Erik

    2013-04-01

    SummaryA study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall-runoff modeling application. The local learning model was first tested on three different catchments: an outdoor experimental catchment measuring 25 m2 (Catchment 1), a small urban catchment 5.6 km2 in size (Catchment 2), and a large rural watershed with area of 241.3 km2 (Catchment 3). The results obtained from the local learning model were comparable or better than results obtained from physically-based, i.e. Kinematic Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The local learning algorithm also required a shorter training time compared to a global learning NFS model. The local learning model was next tested in real-time mode, where the model was continuously adapted when presented with current information in real time. The real-time implementation of the local learning model gave better results, without the need for retraining, when compared to a batch NFS model, where it was found that the batch model had to be retrained periodically in order to achieve similar results.

  13. Design of neuro fuzzy fault tolerant control using an adaptive observer

    International Nuclear Information System (INIS)

    Anita, R.; Umamaheswari, B.; Viswanathan, B.

    2001-01-01

    New methodologies and concepts are developed in the control theory to meet the ever-increasing demands in industrial applications. Fault detection and diagnosis of technical processes have become important in the course of progressive automation in the operation of groups of electric drives. When a group of electric drives is under operation, fault tolerant control becomes complicated. For multiple motors in operation, fault detection and diagnosis might prove to be difficult. Estimation of all states and parameters of all drives is necessary to analyze the actuator and sensor faults. To maintain system reliability, detection and isolation of failures should be performed quickly and accurately, and hardware should be properly integrated. Luenberger full order observer can be used for estimation of the entire states in the system for the detection of actuator and sensor failures. Due to the insensitivity of the Luenberger observer to the system parameter variations, state estimation becomes inaccurate under the varying parameter conditions of the drives. Consequently, the estimation performance deteriorates, resulting in ordinary state observers unsuitable for fault detection technique. Therefore an adaptive observe, which can estimate the system states and parameter and detect the faults simultaneously, is designed in our paper. For a Group of D C drives, there may be parameter variations for some of the drives, and for other drives, there may not be parameter variations depending on load torque, friction, etc. So, estimation of all states and parameters of all drives is carried out using an adaptive observer. If there is any deviation with the estimated values, it is understood that fault has occurred and the nature of the fault, whether sensor fault or actuator fault, is determined by neural fuzzy network, and fault tolerant control is reconfigured. Experimental results with neuro fuzzy system using adaptive observer-based fault tolerant control are good, so as

  14. Fuzzy logic applications to control engineering

    Science.gov (United States)

    Langari, Reza

    1993-12-01

    This paper presents the results of a project presently under way at Texas A&M which focuses on the use of fuzzy logic in integrated control of manufacturing systems. The specific problems investigated here include diagnosis of critical tool wear in machining of metals via a neuro-fuzzy algorithm, as well as compensation of friction in mechanical positioning systems via an adaptive fuzzy logic algorithm. The results indicate that fuzzy logic in conjunction with conventional algorithmic based approaches or neural nets can prove useful in dealing with the intricacies of control/monitoring of manufacturing systems and can potentially play an active role in multi-modal integrated control systems of the future.

  15. Um estudo dos principais modelos de transações em banco de dados móveis e uma proposta diferenciada do modelo pro-motion

    OpenAIRE

    Silva, Edson Carlos da

    2003-01-01

    Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação. Este trabalho apresenta um estudo sobre as principais arquiteturas e modelos de transações móveis. Faz uma revisão sobre os principais problemas encontrados no ambiente móvel, suas características, modelos e propriedades da transação, bem com uma revisão sobre as transações convencionais dos Sistemas Gerenciadores de Banco de Dados Distribuídos # SGBDD # ...

  16. “CHALLENGE LAB” – Um modelo pedagógico em um laboratório multidisciplinar para as engenharias.

    Directory of Open Access Journals (Sweden)

    Arnaldo Ortiz Clemente

    2017-06-01

    Full Text Available Neste artigo é proposto um laboratório, com aplicação de um modelo pedagógico, que utilize como ferramenta “meio” a Robótica, ciência multidisciplinar que possibilita o desenvolvimento prático de habilidades e competências em múltiplos saberes. Os eixos pedagógicos serão: Aprendizado Baseado em Situação Problema; Aprendizado por Mediação e Ensino Hibrido. O objetivo desta proposta é criar um ambiente inovador que leve os Discentes e demais participantes, a desenvolverem soluções para desafios propostos, trabalhando desde o início de sua formação com a aplicação de ferramentas de gestão (processo, produto, projeto, qualidade e inovação, antecipando assim conhecimentos motivadores e motivacionais para seu desenvolvimento individual e coletivo, dando a verdadeira dimensão do papel do Engenheiro e sua atuação na sociedade. Busca-se aqui um processo de aprendizagem para as Engenharias com base na harmonia entre o conceito e a visualização das teorias em práticas prazerosas e desafiadoras, mediadas por ferramentas de gestão de projeto.

  17. Proposta de um modelo de gestão pela qualidade para um abatedouro/frigorifico de suínos

    Directory of Open Access Journals (Sweden)

    Odair José Mombach

    2011-05-01

    Full Text Available O objetivo deste trabalho foi apresentar a importância do fator qualidade na indústria de alimentos e avaliar o processo de produção de um frigorífico/abatedouro de suínos, analisando o atendimento a alguns requisitos de qualidade como as Boas Praticas de Fabricação e o atendimento as exigências do Ministério da Agricultura, Pecuária e Abastecimento (MAPA. Nas vistorias realizadas na entrada sanitária e nos acompanhamentos utilizando o relatório de auditoria do serviço de Inspeção Federal, detectaram-se várias não conformidades. A partir desta constatação, apresenta-se um conjunto de diretrizes para a confecção e implantação de um manual de qualidade, com o objetivo de conscientizar, orientar e direcionar os profissionais envolvidos no processo. Sendo possível obter alimentos seguros, garantindo o atendimento das expectativas dos mercados interno e externo e consequentemente, aumentar a competitividade da empresa. Atendendo assim as Boas Praticas de Fabricação e cumprindo com as exigências legais vigentes, estruturando um modelo de gestão pela qualidade.

  18. Relative Wave Energy based Adaptive Neuro-Fuzzy Inference System model for the Estimation of Depth of Anaesthesia.

    Science.gov (United States)

    Benzy, V K; Jasmin, E A; Koshy, Rachel Cherian; Amal, Frank; Indiradevi, K P

    2018-01-01

    The advancement in medical research and intelligent modeling techniques has lead to the developments in anaesthesia management. The present study is targeted to estimate the depth of anaesthesia using cognitive signal processing and intelligent modeling techniques. The neurophysiological signal that reflects cognitive state of anaesthetic drugs is the electroencephalogram signal. The information available on electroencephalogram signals during anaesthesia are drawn by extracting relative wave energy features from the anaesthetic electroencephalogram signals. Discrete wavelet transform is used to decomposes the electroencephalogram signals into four levels and then relative wave energy is computed from approximate and detail coefficients of sub-band signals. Relative wave energy is extracted to find out the degree of importance of different electroencephalogram frequency bands associated with different anaesthetic phases awake, induction, maintenance and recovery. The Kruskal-Wallis statistical test is applied on the relative wave energy features to check the discriminating capability of relative wave energy features as awake, light anaesthesia, moderate anaesthesia and deep anaesthesia. A novel depth of anaesthesia index is generated by implementing a Adaptive neuro-fuzzy inference system based fuzzy c-means clustering algorithm which uses relative wave energy features as inputs. Finally, the generated depth of anaesthesia index is compared with a commercially available depth of anaesthesia monitor Bispectral index.

  19. Adaptive Neuro-Fuzzy Methodology for Noise Assessment of Wind Turbine

    Science.gov (United States)

    Shamshirband, Shahaboddin; Petković, Dalibor; Hashim, Roslan; Motamedi, Shervin

    2014-01-01

    Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. PMID:25075621

  20. Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.

    Directory of Open Access Journals (Sweden)

    Shahaboddin Shamshirband

    Full Text Available Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

  1. The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach

    Energy Technology Data Exchange (ETDEWEB)

    Al-Hinti, I.; Sakhrieh, A. [Department of Mechanical Engineering, The Hashemite University, Zarqa 13115 (Jordan); Samhouri, M.; Al-Ghandoor, A. [Department of Industrial Engineering, The Hashemite University, Zarqa 13115 (Jordan)

    2009-01-15

    This paper uses a neuro-fuzzy interface system (ANFIS) to study the effect of boost pressure on the efficiency, brake mean effective pressure (BMEP), and the brake specific fuel consumption (BSFC) of a single cylinder diesel engine. Experimental data were used as inputs to ANFIS to simulate the engine performance characteristics. The experimental as well as the model results emphasize the role of boost pressure in improving the different engine characteristics. The results show that the ANFIS technique can be used adequately to identify the effect of boost pressure on the different engine characteristics. In addition, different data points that were not used for ANFIS training were used to validate the developed models. The results suggest that ANFIS can be used accurately to predict the effect of boost pressure on the different engine characteristics. (author)

  2. A new battery capacity indicator for nickel-metal hydride battery powered electric vehicles using adaptive neuro-fuzzy inference system

    International Nuclear Information System (INIS)

    Chau, K.T.; Wu, K.C.; Chan, C.C.; Shen, W.X.

    2003-01-01

    This paper describes a new approach to estimate accurately the battery residual capacity (BRC) of the nickel-metal hydride (Ni-MH) battery for modern electric vehicles (EVs). The key to this approach is to model the Ni-MH battery in EVs by using the adaptive neuro-fuzzy inference system (ANFIS) with newly defined inputs and output. The inputs are the temperature and the discharged capacity distribution describing the discharge current profile, while the output is the state of available capacity (SOAC) representing the BRC. The estimated SOAC from ANFIS model and the measured SOAC from experiments are compared, and the results confirm that the proposed approach can provide an accurate estimation of the SOAC under variable discharge currents

  3. Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing

    CERN Document Server

    Siddique, Nazmul

    2013-01-01

    Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect

  4. Sistem Kontrol Robot Arm 5 DOF Berbasis Pengenalan Pola Suara Menggunakan Mel-Frequency Cepstrum Coefficients (MFCC dan Adaptive Neuro-Fuzzy Inference System (ANFIS

    Directory of Open Access Journals (Sweden)

    WS Mada Sanjaya

    2016-12-01

    Full Text Available Telah dilakukan penelitian yang menggambarkan implementasi pengenalan pola suara untuk mengontrol gerak robot arm 5 DoF dalam mengambil dan menyimpan benda. Dalam penelitian ini metode yang digunakan adalah Mel-Frequency Cepstrum Coefficients (MFCC dan Adaptive Neuro-Fuzzy Inferense System (ANFIS. Metode MFCC digunakan untuk ekstraksi ciri sinyal suara, sedangkan ANFIS digunakan sebagai metode pembelajaran untuk pengenalan pola suara. Pada proses pembelajaran ANFIS data latih yang digunakan sebanyak 6 ciri. Data suara terlatih dan data suara tak terlatih digunakan untuk pengujian sistem pengenalan pola suara. Hasil pengujian menunjukkan tingkat keberhasilan, untuk data suara terlatih sebesar 87,77% dan data tak terlatih sebesar 78,53%. Sistem pengenalan pola suara ini telah diaplikasikan dengan baik untuk mengerakan robot arm 5 DoF berbasis mikrokontroler Arduino. Have been implemented of sound pattern recognition to control 5 DoF of Arm Robot to pick and place an object. In this research used Mel-Frequency Cepstrum Coefficients (MFCC and Adaptive Neuro-Fuzzy Interferense System (ANFIS methods. MFCC method used for features extraction of sound signal, meanwhile ANFIS used to learn sound pattern recognition. On ANFIS method data learning use 6 features. Trained and not trained data used to examine the system of sound pattern identification. The result show the succesfull level, for trained data 87.77% and for not trained data 78.53%. Sound pattern identification system was appliedto controlled 5 DoF arm robot based Arduino microcontroller.

  5. Estimativa da produtividade de café com base em um modelo agrometeorológico-espectral

    Directory of Open Access Journals (Sweden)

    Viviane Gomes Cardoso da Rosa

    2010-12-01

    Full Text Available O objetivo deste trabalho foi avaliar um modelo agrometeorológico-espectral, para estimar a produtividade de cafezais. Utilizaram-se imagens do sensor MODIS e dados agrometeorológicos do modelo regional de previsão do tempo (ETA, para fornecer as variáveis de entrada para o modelo agrometeorológico-espectral da mesorregião geográfica sul/sudoeste do estado de Minas Gerais nos anos-agrícolas de 2003/2004 a 2007/2008. A variável espectral de entrada do modelo agrometeorológico-espectral, índice de área foliar (IAF, usada no cálculo da produtividade máxima, foi estimada com o índice de vegetação por diferença normalizada (NDVI, obtido de imagens MODIS. Outras variáveis de entrada no modelo foram: dados meteorológicos gerados pelo modelo ETA e a capacidade de água disponível no solo. Ao comparar a produtividade média estimada pelo modelo com a fornecida oficialmente pelo IBGE, as diferenças relativas obtidas em escala regional foram de: 0,4, 3,0, 5,3, 1,5 e 8,5% para os anos agrícolas 2003/2004, 2004/2005, 2005/2006, 2006/2007 e 2007/2008, respectivamente. O modelo agrometeorólogico-espectral, que tem como base o modelo de Doorenbos & Kassan, foi tão eficaz para estimar a produtividade dos cafezais quanto o modelo oficial do IBGE. Além disso, foi possível espacializar a quebra de produtividade e prever 80% da produtividade final na primeira quinzena de fevereiro, antes do início da colheita

  6. Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system

    Energy Technology Data Exchange (ETDEWEB)

    Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

    2008-07-01

    This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation of air-conditioning systems. So obtained models will help the system designer to realize this precondition. The most suitable algorithm and neuron number in the hidden layer are found as Levenberg-Marquardt (LM) with seven neurons for ANN model whereas the most suitable membership function and number of membership functions are found as Gauss and two, respectively, for ANFIS model. The root-mean squared (RMS) value and the coefficient of variation in percent (cov) value are 0.0047 and 0.1363, respectively. The absolute fraction of variance (R{sup 2}) is 0.9999 which can be considered as very promising. This paper shows the appropriateness of ANFIS for the quantitative modeling of GCHP systems. (author)

  7. Phase Angle Control of Three Level Inverter Based D-STATCOM Using Neuro-Fuzzy Controller

    Directory of Open Access Journals (Sweden)

    COTELI, R.

    2012-02-01

    Full Text Available Distribution Static Compensator (D-STATCOM is a shunt compensation device used to improve electric power quality in distribution systems. It is well-known that D-STATCOM is a nonlinear, semi-defined and time-varying system. Therefore, control of D-STATCOM by the conventional control techniques is very difficult task. In this paper, the control of D-STATCOM is carried out by the neuro-fuzzy controller (NFC which has non-linear and robust structure. For this aim, an experimental setup based on three-level H-bridge inverter is constructed. Phase angle control method is used for control of D-STATCOM's output reactive power. Control algorithm for this experimental setup is prepared in MATLAB/Simulink and downloaded to DS1103 controller card. A Mamdani type NFC is designed for control of D-STATCOM's reactive current. Output of NFC is integrated to increase tracking performance of controller in steady state. The performance of D-STATCOM is experimentally evaluated by changing reference reactive current as on-line. The experimental results show that the proposed controller gives very satisfactory performance under different loading conditions.

  8. Segurança e produção: um modelo para o planejamento e controle integrado

    OpenAIRE

    Saurin,Tarcisio A.; Formoso,Carlos T.; Guimarães,Lia B. M.

    2002-01-01

    Este artigo apresenta um modelo de planejamento e controle da segurança no trabalho (PCS) integrado ao processo de planejamento e controle da produção (PCP). O modelo foi desenvolvido por meio de dois estudos empíricos em obras industriais, adotando-se a pesquisa ação como estratégia de pesquisa. Os requisitos de segurança são integrados ao PCP em três níveis hierárquicos deste processo. No nível de longo prazo, o planejamento da segurança é configurado pelo desenvolvimento de análises prelim...

  9. Estimating Longitudinal Dispersion Coefficient of Pollutants Using Adaptive Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Hossein Riahi Modvar

    2008-09-01

    Full Text Available Longitudinal dispersion coefficient in rivers and natural streams is usually estimated by simple inaccurate empirical relations because of the complexity of the phenomenon. In this study, the adaptive neuro-fuzzy inference system (ANFIS is used to develop a new flexible tool for predicting the longitudinal dispersion coefficient. The system has the ability to understand and realize the phenomenon without the need for mathematical governing equations.. The training and testing of this new model are accomplished using a set of available published filed data. Several statistical and graphical criteria are used to check the accuracy of the model. The dispersion coefficient values predicted by the ANFIS model compares satisfactorily with the measured data. The predicted values are also compared with those predicted by existing empirical equations reported in the literature to find that the ANFIS model with R2=0.99 and RMSE=15.18 in training stage and R2=0.91 and RMSE=187.8 in testing stage is superior in predicting the dispersion coefficient to the most accurate empirical equation with R2=0.48 and RMSE=295.7. The proposed methodology is a new approach to estimating dispersion coefficient in streams and can be combined with mathematical models of pollutant transfer or real-time updating of these models.

  10. Análise dos modelos didáticos pessoais apresentados por um grupo de licenciandos em Química

    Directory of Open Access Journals (Sweden)

    João Batista Santos Junior

    Full Text Available Resumo: O objetivo do artigo é apresentar e discutir a influência das disciplinas pedagógicas na formação profissional de um grupo de licenciandos do curso de Química de uma universidade federal do estado de São Paulo. Para isso, utilizou-se um instrumento baseado nos modelos didáticos propostos por García Pérez nos anos 2000. Os resultados permitiram a identificação dos modelos didáticos desses alunos e indicaram que alunos que cursaram maior número de disciplinas de natureza didático-pedagógicas apresentaram modelos didáticos pessoais mais organizados e consistentes. Essa análise corrobora com resultados de outros estudos que apontam para a necessidade de articulação das disciplinas científicas com as pedagógicas e possibilitam lançar inferências sobre a construção da grade curricular e o próprio desenho do curso de formação de professores.

  11. Ensemble Genetic Fuzzy Neuro Model Applied for the Emergency Medical Service via Unbalanced Data Evaluation

    Directory of Open Access Journals (Sweden)

    Muammar Sadrawi

    2018-03-01

    Full Text Available Equally partitioned data are essential for prediction. However, in some important cases, the data distribution is severely unbalanced. In this study, several algorithms are utilized to maximize the learning accuracy when dealing with a highly unbalanced dataset. A linguistic algorithm is applied to evaluate the input and output relationship, namely Fuzzy c-Means (FCM, which is applied as a clustering algorithm for the majority class to balance the minority class data from about 3 million cases. Each cluster is used to train several artificial neural network (ANN models. Different techniques are applied to generate an ensemble genetic fuzzy neuro model (EGFNM in order to select the models. The first ensemble technique, the intra-cluster EGFNM, works by evaluating the best combination from all the models generated by each cluster. Another ensemble technique is the inter-cluster model EGFNM, which is based on selecting the best model from each cluster. The accuracy of these techniques is evaluated using the receiver operating characteristic (ROC via its area under the curve (AUC. Results show that the AUC of the unbalanced data is 0.67974. The random cluster and best ANN single model have AUCs of 0.7177 and 0.72806, respectively. For the ensemble evaluations, the intra-cluster and the inter-cluster EGFNMs produce 0.7293 and 0.73038, respectively. In conclusion, this study achieved improved results by performing the EGFNM method compared with the unbalanced training. This study concludes that selecting several best models will produce a better result compared with all models combined.

  12. A família que aparece na mídia: hegemonia de um modelo

    Directory of Open Access Journals (Sweden)

    Hennigen, Inês

    2008-01-01

    Full Text Available A discussão sobre um modelo de familia - aquela constituída por pai, mãe e seus filhos, que aparece de forma recorrente na mídia é o mote deste artigo, cujos dados provêm de uma pesquisa dos discursos da mídia a respeito da paternidade. As análises que realizo, em sintonia com a perspectiva dos Estados Culturais e dos Estados Foucaultianos, focalizam as relações de poder implicadas na produção cultural das redes de significações. Assim, mostro como, ao longo do tempo e a partir de diferentes campos de saber/poder, foi sendo construído e naturalizando este modelo de família, que parece receber o status de familia "normal" em variados produtos midiáticos. Reflexões acerca da importância da mídia no processo de subjetivação no mundo contemporâneo e das implicações subjetivas do caráter hegemônico deste modelo perpassem o texto

  13. Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning

    International Nuclear Information System (INIS)

    Stieler, Florian; Yan, Hui; Lohr, Frank; Wenz, Frederik; Yin, Fang-Fang

    2009-01-01

    Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined. The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be 'translated' to a set of 'if-then rules' for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 ± 0.02%) and membership functions (3.9%), thus suggesting that the 'behavior' of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. The study demonstrated a feasible way

  14. Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning

    Directory of Open Access Journals (Sweden)

    Wenz Frederik

    2009-09-01

    Full Text Available Abstract Background Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI guided system was developed and examined. Methods The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS. Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS, was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints. The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. Results Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 ± 0.02% and membership functions (3.9%, thus suggesting that the "behavior" of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. Conclusion The

  15. Mechanical fault diagnostics for induction motor with variable speed drives using Adaptive Neuro-fuzzy Inference System

    Energy Technology Data Exchange (ETDEWEB)

    Ye, Z. [Department of Electrical & amp; Computer Engineering, Queen' s University, Kingston, Ont. (Canada K7L 3N6); Sadeghian, A. [Department of Computer Science, Ryerson University, Toronto, Ont. (Canada M5B 2K3); Wu, B. [Department of Electrical & amp; Computer Engineering, Ryerson University, Toronto, Ont. (Canada M5B 2K3)

    2006-06-15

    A novel online diagnostic algorithm for mechanical faults of electrical machines with variable speed drive systems is presented in this paper. Using Wavelet Packet Decomposition (WPD), a set of feature coefficients, represented with different frequency resolutions, related to the mechanical faults is extracted from the stator current of the induction motors operating over a wide range of speeds. A new integrated diagnostic system for electrical machine mechanical faults is then proposed using multiple Adaptive Neuro-fuzzy Inference Systems (ANFIS). This paper shows that using multiple ANFIS units significantly reduces the scale and complexity of the system and speeds up the training of the network. The diagnostic algorithm is validated on a three-phase induction motor drive system, and it is proven to be capable of detecting rotor bar breakage and air gap eccentricity faults with high accuracy. The algorithm is applicable to a variety of industrial applications where either continuous on-line monitoring or off-line fault diagnostics is required. (author)

  16. Application of Artificial Neuro-Fuzzy Logic Inference System for Predicting the Microbiological Pollution in Fresh Water

    Science.gov (United States)

    Bouharati, S.; Benmahammed, K.; Harzallah, D.; El-Assaf, Y. M.

    The classical methods for detecting the micro biological pollution in water are based on the detection of the coliform bacteria which indicators of contamination. But to check each water supply for these contaminants would be a time-consuming job and a qualify operators. In this study, we propose a novel intelligent system which provides a detection of microbiological pollution in fresh water. The proposed system is a hierarchical integration of an Artificial Neuro-Fuzzy Inference System (ANFIS). This method is based on the variations of the physical and chemical parameters occurred during bacteria growth. The instantaneous result obtained by the measurements of the variations of the physical and chemical parameters occurred during bacteria growth-temperature, pH, electrical potential and electrical conductivity of many varieties of water (surface water, well water, drinking water and used water) on the number Escherichia coli in water. The instantaneous result obtained by measurements of the inputs parameters of water from sensors.

  17. New type side weir discharge coefficient simulation using three novel hybrid adaptive neuro-fuzzy inference systems

    Science.gov (United States)

    Bonakdari, Hossein; Zaji, Amir Hossein

    2018-03-01

    In many hydraulic structures, side weirs have a critical role. Accurately predicting the discharge coefficient is one of the most important stages in the side weir design process. In the present paper, a new high efficient side weir is investigated. To simulate the discharge coefficient of these side weirs, three novel soft computing methods are used. The process includes modeling the discharge coefficient with the hybrid Adaptive Neuro-Fuzzy Interface System (ANFIS) and three optimization algorithms, namely Differential Evaluation (ANFIS-DE), Genetic Algorithm (ANFIS-GA) and Particle Swarm Optimization (ANFIS-PSO). In addition, sensitivity analysis is done to find the most efficient input variables for modeling the discharge coefficient of these types of side weirs. According to the results, the ANFIS method has higher performance when using simpler input variables. In addition, the ANFIS-DE with RMSE of 0.077 has higher performance than the ANFIS-GA and ANFIS-PSO methods with RMSE of 0.079 and 0.096, respectively.

  18. Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

    Directory of Open Access Journals (Sweden)

    Zhixian Yang

    2014-01-01

    Full Text Available Background electroencephalography (EEG, recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE and sample entropy (SampEn in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.

  19. Type-2 fuzzy neural networks and their applications

    CERN Document Server

    Aliev, Rafik Aziz

    2014-01-01

    This book deals with the theory, design principles, and application of hybrid intelligent systems using type-2 fuzzy sets in combination with other paradigms of Soft Computing technology such as Neuro-Computing and Evolutionary Computing. It provides a self-contained exposition of the foundation of type-2 fuzzy neural networks and presents a vast compendium of its applications to control, forecasting, decision making, system identification and other real problems. Type-2 Fuzzy Neural Networks and Their Applications is helpful for teachers and students of universities and colleges, for scientis

  20. Um estudo da estrutura organizacional e as mudanças organizacionais: proposta de um novo modelo

    Directory of Open Access Journals (Sweden)

    Maria Clara Fraga da Costa

    2012-03-01

    Full Text Available O estudo apresenta como objetivo a proposição de novo modelo de estrutura organizacional, frente às mudanças impostas pela atualidade. A partir de pesquisa bibliográfica sistematizada, evidenciou-se a evolução das estruturas organizacionais dentro da literatura existente sobre o tema. A nova proposta foi denominada estrutura organizacional por conjunto e utiliza a base gráfica de união de conjuntos matemáticos e teóricos sobre a estrutura organizacional de hipertexto.  Ela está fundamentada na ideia de adequação às mudanças e exigência de sustentabilidade das empresas, levando em consideração a possibilidade de crescimento dentro de um ambiente turbulento.

  1. Repensar os modelos de governação dos portos de Setúbal e Lisboa: um contributo

    OpenAIRE

    Estrela, João Maria Pitta da Cunha Calçada

    2014-01-01

    Mestrado em Gestão / CLASSIFICAÇÃO JEL: L91 - Transportation: General; L98 - Government Policy Numa envolvente em que os portos são essenciais para o tráfego mundial de mercadorias e em que Portugal pretende melhorar o sistema portuário e expandir as respetivas infraestruturas, torna-se primordial repensar o modelo de governação dos portos. É pois relevante e atual o objeto deste estudo, que em última análise preconiza a implementação de um novo modelo de governação dos portos de Setúba...

  2. Um modelo para a avaliação da eficiência hídrica em edifícios

    OpenAIRE

    Rodrigues, Carla Andreia Pimentel

    2015-01-01

    A presente dissertação tem como objetivo o desenvolvimento de um modelo para a avaliação da eficiência hídrica de edifícios, à semelhança do já existente noutras especialidades da construção, como é o caso da energia. O desenvolvimento deste modelo integra-se no quadro das preocupações da sociedade atual, no sentido de alargar a todos os sectores as medidas de sustentabilidade ambiental. Embora o esquema proposto se dirija essencialmente a edifícios do tipo residencial, ele ...

  3. A novel power swing blocking scheme using adaptive neuro-fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Zadeh, Hassan Khorashadi; Li, Zuyi [Illinois Institute of Technology, Department of Electrical and Computer Engineering, 3301 S. Dearborn Street, Chicago, IL 60616 (United States)

    2008-07-15

    A power swing may be caused by any sudden change in the configuration or the loading of an electrical network. During a power swing, the impedance locus moves along an impedance circle with possible encroachment into the distance relay zone, which may cause an unnecessary tripping. In order to prevent the distance relay from tripping under such condition, a novel power swing blocking (PSB) scheme is proposed in this paper. The proposed scheme uses an adaptive neuro-fuzzy inference systems (ANFIS) for preventing distance relay from tripping during power swings. The input signals to ANFIS, include the change of positive sequence impedance, positive and negative sequence currents, and power swing center voltage. Extensive tests show that the proposed PSB has two distinct features that are advantageous over existing schemes. The first is that the proposed scheme is able to detect various kinds of power swings thus block distance relays during power swings, even if the power swings are fast or the power swings occur during single pole open conditions. The second distinct feature is that the proposed scheme is able to clear the blocking if faults occur within the relay trip zone during power swings, even if the faults are high resistance faults, or the faults occur at the power swing center, or the faults occur when the power angle is close to 180 . (author)

  4. Analysis prediction of Indonesian banks (BCA, BNI, MANDIRI) using adaptive neuro-fuzzy inference system (ANFIS) and investment strategies

    Science.gov (United States)

    Trianto, Andriantama Budi; Hadi, I. M.; Liong, The Houw; Purqon, Acep

    2015-09-01

    Indonesian economical development is growing well. It has effect for their invesment in Banks and the stock market. In this study, we perform prediction for the three blue chips of Indonesian bank i.e. BCA, BNI, and MANDIRI by using the method of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Takagi-Sugeno rules and Generalized bell (Gbell) as the membership function. Our results show that ANFIS perform good prediction with RMSE for BCA of 27, BNI of 5.29, and MANDIRI of 13.41, respectively. Furthermore, we develop an active strategy to gain more benefit. We compare between passive strategy versus active strategy. Our results shows that for the passive strategy gains 13 million rupiah, while for the active strategy gains 47 million rupiah in one year. The active investment strategy significantly shows gaining multiple benefit than the passive one.

  5. Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system

    Directory of Open Access Journals (Sweden)

    Houman HANACHI

    2018-01-01

    Full Text Available Gas Turbine Engines (GTEs are vastly used for generation of mechanical power in a wide range of applications from airplane propulsion systems to stationary power plants. The gas-path components of a GTE are exposed to harsh operating and ambient conditions, leading to several degradation mechanisms. Because GTE components are mostly inaccessible for direct measurements and their degradation levels must be inferred from the measurements of accessible parameters, it is a challenge to acquire reliable information on the degradation conditions of the parts in different fault modes. In this work, a data-driven fault detection and degradation estimation scheme is developed for GTE diagnostics based on an Adaptive Neuro-Fuzzy Inference System (ANFIS. To verify the performance and accuracy of the developed diagnostic framework on GTE data, an ensemble of measurable gas path parameters has been generated by a high-fidelity GTE model under (a diverse ambient conditions and control settings, (b every possible combination of degradation symptoms, and (c a broad range of signal to noise ratios. The results prove the competency of the developed framework in fault diagnostics and reveal the sensitivity of diagnostic results to measurement noise for different degradation symptoms.

  6. Design of a modified adaptive neuro fuzzy inference system classifier for medical diagnosis of Pima Indians Diabetes

    Science.gov (United States)

    Sagir, Abdu Masanawa; Sathasivam, Saratha

    2017-08-01

    Medical diagnosis is the process of determining which disease or medical condition explains a person's determinable signs and symptoms. Diagnosis of most of the diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system with Modified Levenberg-Marquardt algorithm using analytical derivation scheme for computation of Jacobian matrix. The goal is to investigate how certain diseases are affected by patient's characteristics and measurement such as abnormalities or a decision about presence or absence of a disease. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent system was tested with Pima Indian Diabetes dataset obtained from the University of California at Irvine's (UCI) machine learning repository. The proposed method's performance was evaluated based on training and test datasets. In addition, an attempt was done to specify the effectiveness of the performance measuring total accuracy, sensitivity and specificity. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.

  7. Performance of a neuro-fuzzy model in predicting weight changes of chronic schizophrenic patients exposed to antipsychotics.

    Science.gov (United States)

    Lan, T H; Loh, E W; Wu, M S; Hu, T M; Chou, P; Lan, T Y; Chiu, H-J

    2008-12-01

    Artificial intelligence has become a possible solution to resolve the problem of loss of information when complexity of a disease increases. Obesity phenotypes are observable clinical features of drug-naive schizophrenic patients. In addition, atypical antipsychotic medications may cause these unwanted effects. Here we examined the performance of neuro-fuzzy modeling (NFM) in predicting weight changes in chronic schizophrenic patients exposed to antipsychotics. Two hundred and twenty inpatients meeting DSMIV diagnosis of schizophrenia, treated with antipsychotics, either typical or atypical, for more than 2 years, were recruited. All subjects were assessed in the same study period between mid-November 2003 and mid-April 2004. The baseline and first visit's physical data including weight, height and circumference were used in this study. Clinical information (Clinical Global Impression and Life Style Survey) and genotype data of five single nucleotide polymorphisms were also included as predictors. The subjects were randomly assigned into the first group (105 subjects) and second group (115 subjects), and NFM was performed by using the FuzzyTECH 5.54 software package, with a network-type structure constructed in the rule block. A complete learned model trained from merged data of the first and second groups demonstrates that, at a prediction error of 5, 93% subjects with weight gain were identified. Our study suggests that NFM is a feasible prediction tool for obesity in schizophrenic patients exposed to antipsychotics, with further improvements required.

  8. Forecasting Water Level Fluctuations of Urmieh Lake Using Gene Expression Programming and Adaptive Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Sepideh Karimi

    2012-06-01

    Full Text Available Forecasting lake level at various prediction intervals is an essential issue in such industrial applications as navigation, water resource planning and catchment management. In the present study, two data driven techniques, namely Gene Expression Programming and Adaptive Neuro-Fuzzy Inference System, were applied for predicting daily lake levels for three prediction intervals. Daily water-level data from Urmieh Lake in Northwestern Iran were used to train, test and validate the used techniques. Three statistical indexes, coefficient of determination, root mean square error and variance accounted for were used to assess the performance of the used techniques. Technique inter-comparisons demonstrated that the GEP surpassed the ANFIS model at each of the prediction intervals. A traditional auto regressive moving average model was also applied to the same data sets; the obtained results were compared with those of the data driven approaches demonstrating superiority of the data driven models to ARMA.

  9. Uma análise da influência da estocasticidade das informações sobre um modelo de programação linear

    Directory of Open Access Journals (Sweden)

    Neida Maria Patias Volpi

    2000-06-01

    Full Text Available Neste trabalho foi discutido o impacto de perturbações estocásticas em um modelo de planejamento florestal. Foi desenvolvido um modelo de programação linear e uma abordagem, através de simulações estocásticas, para analisar e quantificar a variabilidade que ocorre nos valores da função objetivo, perante a natureza estocástica dos dados que alimentam o modelo. Um programa para efetuar tais simulações foi desenvolvido. O conceito de razão de variabilidade objeto-sistema foi introduzido para medir a suscetibilidade do sistema em relação às variações ocorridas no objeto.The impact of stochastic perturbations in a forest planning model was discussed in this work. It was developed a linear programming and an approach, through stochastic simulations, to analyse and quantify the variability that occurs in the values of the objective function, because of the nature of the data that are used in the model. A program to do such simulations was developed. A new concept was introduced, called object-system ratio of variability, in order to measure the sensitivity of the response of the system when variations occur in the object.

  10. Neuro-fuzzy GMDH based particle swarm optimization for prediction of scour depth at downstream of grade control structures

    Directory of Open Access Journals (Sweden)

    Mohammad Najafzadeh

    2015-03-01

    Full Text Available In the present study, neuro-fuzzy based-group method of data handling (NF-GMDH as an adaptive learning network was utilized to predict the maximum scour depth at the downstream of grade-control structures. The NF-GMDH network was developed using particle swarm optimization (PSO. Effective parameters on the scour depth include sediment size, geometry of weir, and flow characteristics in the upstream and downstream of structure. Training and testing of performances were carried out using non-dimensional variables. Datasets were divided into three series of dataset (DS. The testing results of performances were compared with the gene-expression programming (GEP, evolutionary polynomial regression (EPR model, and conventional techniques. The NF-GMDH-PSO network produced lower error of the scour depth prediction than those obtained using the other models. Also, the effective input parameter on the maximum scour depth was determined through a sensitivity analysis.

  11. Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

    Science.gov (United States)

    Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

    2013-09-01

    This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

  12. O DISCURSO DIDÁTICO: TESTAGEM DE UM MODELO PARA DESCRIÇÃO DO SENTIDO PELA SEMÂNTICA ARGUMENTATIVA

    Directory of Open Access Journals (Sweden)

    Tânia Maris de Azevedo

    2016-01-01

    Full Text Available Pensar a leitura e a produção de textos/discursos implica necessariamente pensar a construção do sentido. Seguindo esse raciocínio, Azevedo elaborou sua tese de doutorado (semântica Argumentativa – uma possibilidade para a descrição do sentido do discurso, em que propôs o redimensionamento de alguns conceitos metodológico-operacionais da Teoria da Argumentação na Língua, de Oswald Ducrot e Marion Carel – mais especificamente, da Teoria dos Blocos Semânticos –, com a finalidade de aplicá-la à descrição semântico-argumentativa do discurso. Tal redimensionamento originou um modelo teórico-metodológico para a escrição do sentido do discurso. consequência natural de uma investigação é a testagem do modelo criado. Essa testagem constitui-se no cerne dapesquisa desenvolvida por Azevedo e Rowell, que, por sua vez, é apenas a primeira etapa de um estudo bem mais amplo rumo à transposição didática do referido modelo para o ensino de língua materna, fim último e essencial do trabalho iniciado com a tese de Azevedo. Palavras-chave: descrição semântico-argumentativa; Ensino; discurso didático.

  13. Using adaptive neuro fuzzy inference system (ANFIS) for proton exchange membrane fuel cell (PEMFC) performance modeling

    International Nuclear Information System (INIS)

    Rezazadeh, S.; Mirzaee, I.; Mehrabi, M.

    2012-01-01

    In this paper, an adaptive neuro fuzzy inference system (ANFIS) is used for modeling proton exchange membrane fuel cell (PEMFC) performance using some numerically investigated and compared with those to experimental results for training and test data. In this way, current density I (A/cm 2 ) is modeled to the variation of pressure at the cathode side P C (atm), voltage V (V), membrane thickness (mm), Anode transfer coefficient α an , relative humidity of inlet fuel RH a and relative humidity of inlet air RH c which are defined as input (design) variables. Then, we divided these data into train and test sections to do modeling. We instructed ANFIS network by 80% of numerical validated data. 20% of primary data which had been considered for testing the appropriateness of the models was entered ANFIS network models and results were compared by three statistical criterions. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can be expanded for more general states

  14. Using adaptive neuro fuzzy inference system (ANFIS) for proton exchange membrane fuel cell (PEMFC) performance modeling

    Energy Technology Data Exchange (ETDEWEB)

    Rezazadeh, S.; Mirzaee, I. [Urmia Univ., Urmia (Iran, Islamic Republic of); Mehrabi, M. [University of Pretoria, Pretoria (South Africa)

    2012-11-15

    In this paper, an adaptive neuro fuzzy inference system (ANFIS) is used for modeling proton exchange membrane fuel cell (PEMFC) performance using some numerically investigated and compared with those to experimental results for training and test data. In this way, current density I (A/cm{sup 2}) is modeled to the variation of pressure at the cathode side P{sup C} (atm), voltage V (V), membrane thickness (mm), Anode transfer coefficient {alpha}{sup an}, relative humidity of inlet fuel RH{sup a} and relative humidity of inlet air RH{sup c} which are defined as input (design) variables. Then, we divided these data into train and test sections to do modeling. We instructed ANFIS network by 80% of numerical validated data. 20% of primary data which had been considered for testing the appropriateness of the models was entered ANFIS network models and results were compared by three statistical criterions. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can be expanded for more general states.

  15. Improved Trust Prediction in Business Environments by Adaptive Neuro Fuzzy Inference Systems

    Directory of Open Access Journals (Sweden)

    Ali Azadeh

    2015-06-01

    Full Text Available Trust prediction turns out to be an important challenge when cooperation among intelligent agents with an impression of trust in their mind, is investigated. In other words, predicting trust values for future time slots help partners to identify the probability of continuing a relationship. Another important case to be considered is the context of trust, i.e. the services and business commitments for which a relationship is defined. Hence, intelligent agents should focus on improving trust to provide a stable and confident context. Modelling of trust between collaborating parties seems to be an important component of the business intelligence strategy. In this regard, a set of metrics have been considered by which the value of confidence level for predicted trust values has been estimated. These metrics are maturity, distance and density (MD2. Prediction of trust for future mutual relationships among agents is a problem that is addressed in this study. We introduce a simulation-based model which utilizes linguistic variables to create various scenarios. Then, future trust values among agents are predicted by the concept of adaptive neuro-fuzzy inference system (ANFIS. Mean absolute percentage errors (MAPEs resulted from ANFIS are compared with confidence levels which are determined by applying MD2. Results determine the efficiency of MD2 for forecasting trust values. This is the first study that utilizes the concept of MD2 for improvement of business trust prediction.

  16. Condutas desviantes e traços de personalidade: testagem de um modelo causal

    OpenAIRE

    Vasconcelos,Tatiana Cristina; Gouveia,Valdiney Veloso; Pimentel,Carlos Eduardo; Pessoa,Viviany Silva

    2008-01-01

    A meta principal deste estudo foi comprovar a adequação de um modelo causal à explicação de comportamentos socialmente desviantes (condutas anti-sociais e delitivas), considerando a contribuição dos traços de personalidade (neuroticismo, extroversão e busca de sensações). Para tanto, participaram 755 estudantes do Ensino Médio e Superior, sendo a maioria do sexo feminino (50,3%), de escolas privadas (53,0%) e com idades variando de 16 a 26 anos (média=20,1; desvio-padrão=3,12). Estes responde...

  17. A neuro-fuzzy controlling algorithm for wind turbine

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Li [Tampere Univ. of Technology (Finland); Eriksson, J T [Tampere Univ. of Technology (Finland)

    1996-12-31

    The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)

  18. A neuro-fuzzy controlling algorithm for wind turbine

    Energy Technology Data Exchange (ETDEWEB)

    Li Lin [Tampere Univ. of Technology (Finland); Eriksson, J.T. [Tampere Univ. of Technology (Finland)

    1995-12-31

    The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)

  19. Applied to neuro-fuzzy models for signal validation in Angra 1 nuclear power plant; Modelos de validacao de sinal utilizando tecnicas de inteligencia artificial aplicados a um reator nuclear

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Mauro Vitor de

    1999-06-15

    This work develops two models of signal validation in which the analytical redundancy of the monitored signals from an industrial plant is made by neural networks. In one model the analytical redundancy is made by only one neural network while in the other it is done by several neural networks, each one working in a specific part of the entire operation region of the plant. Four cluster techniques were tested to separate the entire region of operation in several specific regions. An additional information of systems' reliability is supplied by a fuzzy inference system. The models were implemented in C language and tested with signals acquired from Angra I nuclear power plant, from its start to 100% of power. (author)

  20. Sequência generalizada de Pell (SGP: aspectos históricos e epistemológicos sobre a evolução de um modelo

    Directory of Open Access Journals (Sweden)

    Francisco Regis Vieira Alves

    2016-09-01

    Full Text Available Neste artigo trazemos uma discussão, que não desconsidera elementos de ordem histórica, atinentes ao modelo matemático que nomeamos por Sequência de Pell – SP. Mostraremos os resultados de artigos especializados, predominantemente das décadas de 80 e 90, que indicam resultados que validam a generalização do referido modelo, e que passa a ser nomeado, então, como Sequência Generalizada de Pell – SGP. Intimamente relacionado com outra duas outras sequências emblemáticas, como a sequência de Fibonacci e de Lucas, apontaremos propriedades e relações desconsideradas por autores de livros de História da Matemática - HM e demarcaremos determinados elementos que podem atuar decidamente para um entendimento da evolução epistemológica de um modelo devido a John Pell, o qual preserva seu vigor e se mostra indene ao passar dos séculos.

  1. RETRACTED: Adaptive neuro-fuzzy prediction of modulation transfer function of optical lens system

    Science.gov (United States)

    Petković, Dalibor; Shamshirband, Shahaboddin; Anuar, Nor Badrul; Md Nasir, Mohd Hairul Nizam; Pavlović, Nenad T.; Akib, Shatirah

    2014-07-01

    This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor. Sections ;1. Introduction; and ;2. Modulation transfer function;, as well as Figures 1-3, plagiarize the article published by N. Gül and M. Efe in Turk J Elec Eng & Comp Sci 18 (2010) 71 (http://journals.tubitak.gov.tr/elektrik/issues/elk-10-18-1/elk-18-1-6-0811-9.pdf). Sections ;4. Adaptive neuro-fuzzy inference system; and ;6. Conclusion; duplicate parts of the articles previously published by the corresponding author et al in ;Expert Systems with Applications; 39 (2012) 13295-13304, http://dx.doi.org/10.1016/j.eswa.2012.05.072 and ;Expert Systems with Applications; 40 (2013) 281-286, http://dx.doi.org/10.1016/j.eswa.2012.07.076. One of the conditions of submission of a paper for publication is that authors declare explicitly that the paper is not under consideration for publication elsewhere. Re-use of any data should be appropriately cited. As such this article represents an abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.

  2. Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping

    Science.gov (United States)

    Park, Inhye; Choi, Jaewon; Jin Lee, Moung; Lee, Saro

    2012-11-01

    We constructed hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok City, Korea, using an adaptive neuro-fuzzy inference system (ANFIS) and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, and ground subsidence maps. An attribute database was also constructed from field investigations and reports on existing ground subsidence areas at the study site. Five major factors causing ground subsidence were extracted: (1) depth of drift; (2) distance from drift; (3) slope gradient; (4) geology; and (5) land use. The adaptive ANFIS model with different types of membership functions (MFs) was then applied for ground subsidence hazard mapping in the study area. Two ground subsidence hazard maps were prepared using the different MFs. Finally, the resulting ground subsidence hazard maps were validated using the ground subsidence test data which were not used for training the ANFIS. The validation results showed 95.12% accuracy using the generalized bell-shaped MF model and 94.94% accuracy using the Sigmoidal2 MF model. These accuracy results show that an ANFIS can be an effective tool in ground subsidence hazard mapping. Analysis of ground subsidence with the ANFIS model suggests that quantitative analysis of ground subsidence near AUCMs is possible.

  3. An Ultrasonic Multi-Beam Concentration Meter with a Neuro-Fuzzy Algorithm for Water Treatment Plants.

    Science.gov (United States)

    Lee, Ho-Hyun; Jang, Sang-Bok; Shin, Gang-Wook; Hong, Sung-Taek; Lee, Dae-Jong; Chun, Myung Geun

    2015-10-23

    Ultrasonic concentration meters have widely been used at water purification, sewage treatment and waste water treatment plants to sort and transfer high concentration sludges and to control the amount of chemical dosage. When an unusual substance is contained in the sludge, however, the attenuation of ultrasonic waves could be increased or not be transmitted to the receiver. In this case, the value measured by a concentration meter is higher than the actual density value or vibration. As well, it is difficult to automate the residuals treatment process according to the various problems such as sludge attachment or sensor failure. An ultrasonic multi-beam concentration sensor was considered to solve these problems, but an abnormal concentration value of a specific ultrasonic beam degrades the accuracy of the entire measurement in case of using a conventional arithmetic mean for all measurement values, so this paper proposes a method to improve the accuracy of the sludge concentration determination by choosing reliable sensor values and applying a neuro-fuzzy learning algorithm. The newly developed meter is proven to render useful results from a variety of experiments on a real water treatment plant.

  4. Um modelo semântico de publicações eletrônicas | A semantic model for electronic publishing

    Directory of Open Access Journals (Sweden)

    Carlos Henrique Marcondes

    2011-03-01

    Full Text Available Resumo Publicações eletrônicas, apesar dos avanços das Tecnologias da Informação, são ainda calcados no modelo impresso. O formato textual impede que programas possam ser usados para o processamento “semântico” desses conteúdos. È porposto um modelo “semântico” de publicações cientificas eletrônicas, no qual as conclusões contidas no texto do artigo fornecidas por autores e representadas em formato “inteligível” por programas, permitindo recuperação semântica, identificação de indícios de novas descobertas científicas e de incoerências sobre este conhecimento. O modelo se baseia nos conceitos de estrutura profunda, ou semântica, da linguagem (CHOMSKY, 1975, de microestrutura, macroestrutura e superestrutura, (KINTSH, VAN DIJK, 1972, na estrutura retórica de artigos científicos (HUTCHINS, 1977, (GROSS, 1990 e nos elementos de metodologia cientifica, como problema, questão, objetivo, hipótese, experimento e conclusão. Resulta da análise de 89 artigos biomédicos. Foi desenvolvido um protótipo de sistema que implementa parcialmente o modelo. Questionários foram usados com autores para embasar o desenvolvimento do protótipo. O protótipo foi testando com pesquisadores-autores. Foram identificados quatro padrões de raciocínio e encadeamento dos elementos semânticos em artigos científicos. O modelo de conteúdo foi implementado como uma ontologia computacional. Foi desenvolvido e avaliado um protótipo de uma interface web de submissão artigos pelos autores a um sistema eletrônico de publicação de periódicos que implementa o modelo. Palavras-chave publicações eletrônicas; metodológica científica; comunicação científica; representação do conhecimento; ontologias; processamento semântico de conteúdos; e-Ciência Abstract Electronic publishing, although Information Technologies advancements, are still based in the print text model. The textual format prevents programs to semantic process

  5. Fuzzy model predictive control algorithm applied in nuclear power plant

    International Nuclear Information System (INIS)

    Zuheir, Ahmad

    2006-01-01

    The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

  6. Avaliação de um modelo de intervenção psicológica para meninas vítimas de abuso sexual

    Directory of Open Access Journals (Sweden)

    Luísa F. Habigzang

    Full Text Available O abuso sexual contra crianças e adolescentes é considerado um importante problema de saúde pública. Dessa forma, é necessário o desenvolvimento de estudos que avaliem a efetividade de modelos de intervenção psicológica. O presente estudo tem como objetivo avaliar um modelo de grupoterapia cognitivo-comportamental para meninas vítimas de abuso sexual. Participaram 10 meninas com idade entre 9 e 13 anos, vítimas de pelo menos um episódio de abuso sexual intrafamiliar. O tipo de abuso sexual variou entre os casos, sendo que, em sete, ocorreram manipulação de genitais e assédio e, em três, relações sexuais com penetração. O delineamento utilizado foi medidas repetidas, realizadas antes, durante e depois da intervenção. Foram avaliados sintomas de depressão, ansiedade, estresse e transtorno do estresse pós-traumático. O modelo avaliado é constituído por 20 sessões com atividades semi-estruturadas. Os resultados apontaram a redução significativa dos sintomas e a reestruturação de crenças disfuncionais relacionadas ao abuso.

  7. UM MODELO DE LOCALIZAÇÃO/DISTRIBUIÇÃO NUMA REDE LOGÍSTICA

    Directory of Open Access Journals (Sweden)

    Sueli Bacchi Machado

    2010-05-01

    Full Text Available

    A determinação dos locais ótimos para instalação de terminais (depósitos, armazéns de triagem e distribuição de carga, numa rede logística, é um problema clássico que tem gerado um grande numero de trabalhos de pesquisa. No entanto, ao se considerar o custo logístico global, que inclui as despesas de transporte, de triagem, de coleta/entrega e de estoque, a estratégia ótima de distribuição vai variar com o tipo de produto, os fluxos, os tempos, etc. Em alguns casos será mais conveniente efetuar as entregas através de viagens diretas industrias/consumidor; noutros, a distribuição via deposito se apresentara como a mais adequada.O modelo apresentado neste trabalho procura resolver simultaneamente: (a o problema de localizar K terminais numa rede logística; (b se1ecionar a estratégia ótima de distribuição para cada par produtor/consumidor, considerando transferência direta ou distribuição consolidada via um dos terminais. O método heurístico desenvolvido mostrou-se adequado, tendo sido testado em redes diversas, de porte elevado.

  8. A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data

    Science.gov (United States)

    Ashrafi, Mohammad; Chua, Lloyd Hock Chye; Quek, Chai; Qin, Xiaosheng

    2017-02-01

    Current state-of-the-art online neuro fuzzy models (NFMs) such as DENFIS (Dynamic Evolving Neural-Fuzzy Inference System) have been used for runoff forecasting. Online NFMs adopt a local learning approach and are able to adapt to changes continuously. The DENFIS model however requires upper/lower bound for normalization and also the number of rules increases monotonically. This requirement makes the model unsuitable for use in basins with limited data, since a priori data is required. In order to address this and other drawbacks of current online models, the Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) is adopted in this study for forecast applications in basins with limited data. GSETSK is a fully-online NFM which updates its structure and parameters based on the most recent data. The model does not require the need for historical data and adopts clustering and rule pruning techniques to generate a compact and up-to-date rule-base. GSETSK was used in two forecast applications, rainfall-runoff (a catchment in Sweden) and river routing (Lower Mekong River) forecasts. Each of these two applications was studied under two scenarios: (i) there is no prior data, and (ii) only limited data is available (1 year for the Swedish catchment and 1 season for the Mekong River). For the Swedish Basin, GSETSK model results were compared to available results from a calibrated HBV (Hydrologiska Byråns Vattenbalansavdelning) model. For the Mekong River, GSETSK results were compared against the URBS (Unified River Basin Simulator) model. Both comparisons showed that results from GSETSK are comparable with the physically based models, which were calibrated with historical data. Thus, even though GSETSK was trained with a very limited dataset in comparison with HBV or URBS, similar results were achieved. Similarly, further comparisons between GSETSK with DENFIS and the RBF (Radial Basis Function) models highlighted further advantages of GSETSK as having a rule-base (compared to

  9. Desenvolvimento de um modelo para avaliar a sustentabilidade corporativa Development of a model for corporate sustainability assesment

    Directory of Open Access Journals (Sweden)

    Fábio Cristiano Zamcopé

    2012-08-01

    Full Text Available Este trabalho apresenta o desenvolvimento de um modelo de avaliação da sustentabilidade corporativa, construído com base no sistema de valores, interesses e preferências dos decisores de uma indústria têxtil. Como instrumento de intervenção utilizou-se a metodologia multicritério de apoio à decisão - construtivista (MCDA-C, por essa possibilitar a identificação de um conjunto de indicadores de desempenho que caracterizam a sustentabilidade e por mensurar o grau de alcance das propriedades analisadas da organização quanto ao seu compromisso com a sustentabilidade. O modelo possibilitou aos decisores a compreensão das principais questões de sustentabilidade e ações necessárias para resolvê-las, além de proporcionar a medição de desempenho e avaliação do progresso para melhorias contínuas. Entre as ações geradas a partir do modelo, destaca-se a criação de um comitê de sustentabilidade, revisão do planejamento estratégico, fortalecimento dos canais de comunicação e programas de conscientização para os funcionários, fornecedores e demais envolvidos.This paper introduces the development of an assessment model for corporate sustainability, which was built based on the system of values, concerns and preferences of decision makers from a textile factory. The Multi-criteria for Decision Aid - Constructivist (MCDA-C methodology was used, enabling to identify a set of performance indicators that features the company's sustainability and to measure the degree of properties range considered by the organization about its commitment to sustainability. The model allowed decision makers to understand the key sustainability issues and actions needed to resolve them, and provide performance measurement and evaluation of progress toward continuous improvement. Among the actions generated by the model, there are the creation of a committee of sustainability, strategic planning review, strengthening of communication channels and

  10. Improving detection of dairy cow estrus using fuzzy logic Melhoria da detecção do estro de vacas leiteiras por meio da lógica fuzzy

    Directory of Open Access Journals (Sweden)

    Leandro dos Anjos Brunassi

    2010-10-01

    detectado visualmente, um método considerado ineficiente. Alguns estudos têm sido desenvolvidos com o intuito de se obter modelos efetivos para interpretar a ocorrência e detecção do estro, contudo, muitos modelos apresentam alertas falsos positivos, sendo muitas vezes considerados falhos. Construiu-se um sistema baseado nas funções de inferência fuzzy capaz de detectar eficientemente o estro de vacas de leite, avaliando seu desempenho com curvas ROC (Receiver-Operating Characteristic. Os dados de entrada do sistema combinaram informações de casos prévios de estro, aplicações de prostaglandina com dados das atividades das vacas. As saídas do sistema foram organizadas em três categorias: "em estro", "talvez em estro" e "sem estro". A validação do sistema foi realizada em uma granja leiteira comercial utilizando um rebanho de 350 vacas em lactação. O desempenho do teste foi avaliado calculando a sensibilidade na detecção correta de estro; e sua especificidade através da precisão da detecção. O teste teve uma duração de seis meses, sendo analisados mais de 25 mil casos de estro da base de dados da granja. A sensibilidade obtida foi de 84,2%, indicando que o sistema pode detectar eficientemente o estro melhorando a detecção automática em vacas leiteiras.

  11. Influencia local em modelos de series temporais

    OpenAIRE

    Bruno Reis dos Santos

    2008-01-01

    Resumo: Nesta dissertação é discutido o uso da metodologia de diagnóstico de Influência Local em modelos de séries temporais. Especificamente, serão estudados os modelos autoregressivos de ordem um, os modelos de regressão com erros autoregressivos de ordem um e modelos de longa-memória. As medidas de influência local consideradas são: Inclinação de Billor e Loynes e Curvatura de Cook. As principais contribuições nesta dissertação são duas. Primeiro, a utilização da metodologia de limiares (b...

  12. Avaliação de necessidades de TD&E: proposição de um novo modelo.

    Directory of Open Access Journals (Sweden)

    Gardênia da Silva Abbad

    2012-12-01

    Full Text Available Análises de necessidades de treinamento (ANTs, apesar de importantes, têm sido realizadas de modo pouco sistemático em ambientes organizacionais. A análise da literatura mostra que a produção intelectual nessa área está restrita a análises nos níveis do indivíduo e das tarefas, com poucas pesquisas enfocando necessidades em níveis mais abrangentes como grupos, equipes ou macroprocessos organizacionais. De modo geral, as abordagens de análise de necessidades ainda estão focadas em cargos ou nas estruturas organizacionais rígidas, estáticas, burocráticas (departamentos, divisões etc.. Essa característica torna a ANT anacrônica e pouco útil, pois não capta novos arranjos organizacionais como arquiteturas matriciais, organização em rede, organizações virtuais ou novas configurações dos trabalhos humanos. Esse caráter dinâmico implica uma mudança no caráter da ANT, para uma abordagem mais prospectiva, voltada à aprendizagem de habilidades complexas e de alta volatilidade. Este ensaio teórico objetiva rever a literatura da área e propor um modelo de diagnóstico de necessidades de treinamento baseado em taxonomia de resultados de aprendizagem e com foco em diversos níveis de análise. O modelo de ANT proposto deriva de um conjunto de resultados de pesquisas empíricas e vai além da análise de tarefas e de pessoas, incluindo também o nível organizacional e de grupo. A aplicação deste modelo permite fornecer as informações necessárias ao desenho de situações de aprendizagem e de treinamento que possam promover o desenvolvimento de complexas competências valorizadas pelo mundo do trabalho, cujo desenvolvimento requer ações educacionais variadas, currículos e programas de aprendizagem contínua e de educação permanente (ao longo de toda a vida. Nesse sentido, o modelo proposto permite que seja feito um elo entre os estudos empíricos sobre o tema e a sua aplicação prática em organizações de

  13. Um modelo evolucionário setorial

    Directory of Open Access Journals (Sweden)

    Mario L. Possas

    2001-09-01

    Full Text Available Este artigo apresenta um modelo evolucionário setorial de simulação que combina microfundamentos neo-schumpeterianos e pós-keynesianos e expõe alguns resultados preliminares das primeiras simulações. Estes resultados apontam, quanto a distintas condições do ambiente de mercado: o efeito positivo da taxa de crescimento da produtividade na fronteira tecnológica sobre a concentração de mercado; o efeito ambíguo do aprendizado, capaz de produzir um lock-in de firmas inovadoras pioneiras em tecnologias que se tornam eventualmente obsoletas; o efeito "anti-seletivo" de taxas de juros muito elevadas, endividando e prejudicando o esforço de investimento e o sucesso competitivo das inovadoras em benefício das imitadoras. Quanto à simulação das estratégias de preços, constatou-se: a maior seletividade de estratégias em que as firmas inovadoras dão mais peso ao mark-up desejado, gerando maior concentração de mercado, efeito que é atenuado pelo crescimento da demanda e pelo aprendizado; a importância, para a sobrevivência de empresas menos inovadoras, do feedback do desempenho competitivo sobre o mark-up desejado e o efetivo, aumentando a adaptabilidade das empresas às condições de seleção do mercado.This paper presents a sectoral evolutionary simulation model combining neo-Schumpeterian and Post-Keynesian microfoundations, and exhibits some preliminary results. As to different market environment conditions, the simulations suggest: a positive effect of productivity growth of the technological frontier on market concentration; an ambiguous result of "learning by doing", from which innovation leaders may be found "locked in" new technologies that eventually become obsolete; a "counter-selective" effect of very high interest rates which may increase innovators indebtedness to the extent of impairing their investment efforts and competitive success as compared to some imitators. As to different price strategies, major findings

  14. Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

    Science.gov (United States)

    El-Sebakhy, Emad A.

    2009-09-01

    Pressure-volume-temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson-Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.

  15. As relações de poder em equipe multiprofissional de Saúde da Família segundo um modelo teórico arendtiano

    Directory of Open Access Journals (Sweden)

    Hadelândia Milon de Oliveira

    2011-06-01

    Full Text Available A Estratégia Saúde da Família (ESF está fundamentada no trabalho em equipe, exigindo mudanças no processo de trabalho das Unidades de Saúde, distanciando-se do modelo centralizado na figura do médico. Este trabalho objetiva propor um modelo teórico de avaliação da relação de poder na equipe multiprofissional, com base nos trabalhos de Hannah Arendt. Baseando-se em análise de documentos legais da ESF e dos pressupostos arenditianos, estabeleceu-se um modelo teórico para avaliação da ESF, utilizando-se a abordagem qualitativa com enfoque hermenêutico-dialético. Para testar o modelo, aplicaram-se grupos focais com as equipes de saúde e entrevistas individuais no município de Manacapuru - interior do Estado do Amazonas. Os resultados mostram que cada profissional exerce sua função de forma isolada, não compartilhada; a política da gestão municipal é limitadora do trabalho coletivo, não governando com liderança, mas se configurando como tirania na perspectiva arendtiana, trazendo descrédito à ESF.

  16. Proposta de um modelo de gestão integrada da cadeia de suprimentos: aplicação no segmento de eletrodomésticos

    Directory of Open Access Journals (Sweden)

    Reinaldo Fagundes dos Santos

    2015-03-01

    Full Text Available O espectro da competitividade entre as organizações manufatureiras, numa economia cada vez mais globalizada, já não se limita ao jogo de forças entre as organizações concorrentes, mas acontece de fato entre cadeias de suprimentos. Entretanto, os atuais modelos de gestão da cadeia de suprimentos não vêm cumprindo seu papel, havendo grande disparidade entre os potenciais benefícios e a prática. O presente trabalho tem como objetivo propor um modelo de SCM e seu método de implementação, visando aumentar a capacidade de resposta ao mercado consumidor das empresas manufatureiras. O modelo proposto utiliza os conceitos da Manufatura Enxuta, da Teoria das Restrições e do modelo SCOR em um ambiente de Tecnologia da Informação e Comunicação. São aplicados também os conceitos do Triple Bottom Line na busca da sustentabilidade. A avaliação de sua eficácia é feita através de uma aplicação no segmento de eletrodomésticos, permitindo sugerir a ampliação do escopo de sua utilização.

  17. Um mapeamento descritivo dos modelos de gestão de redes de correspondentes bancários no Brasil

    Directory of Open Access Journals (Sweden)

    Martin Jayo

    2013-09-01

    Full Text Available Apesar dos diversos estudos que se desenvolveram nos últimos anos enfocando o crescimento dos chamados correspondentes bancários (CBs como canal de distribuição de serviços financeiros à população de baixa renda no Brasil, existe uma importante lacuna nessa literatura, relacionada à compreensão das diferentes formas pelas quais redes de CBs podem ser montadas e geridas. No presente artigo teve-se por objetivo construir um mapeamento das diferentes configurações de negócios - ou modelos de gestão de redes de CBs - hoje praticadas na utilização do canal de CBs pelos bancos brasileiros. Para tanto, adotou-se um método taxonômico, que permitiu distinguir a existência de seis diferentes modelos de gestão de redes de CBs praticados pelos bancos na gestão do canal, os quais foram, por sua vez, agrupados em três classes, de acordo com o grau de delegação de atividades a gestores de rede terceirizados. O resultado do trabalho é um mapeamento inédito das diferentes configurações de negócios praticadas na operação do canal de CBs no Brasil. A relevância da contribuição deriva da escassez de referências ao tópico em estudos anteriores, bem como da importância adquirida em anos recentes pelo canal de CBs como um dos principais canais de distribuição de serviços bancários à população de baixa renda.

  18. 1 RESEARCH ARTICLE Neuro-Fuzzy Model of Homocysteine ...

    Indian Academy of Sciences (India)

    2017-03-10

    Mar 10, 2017 ... metabolism and investigated the influence of life style modulations in controlling ... fuzzy model showed higher accuracy in predicting homocysteine with a ... The dietary source of folate is in the form of folyl polyglutamate and is .... protein and the ligands were optimized by Drug Discovery studio version 3.0.

  19. Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach

    Science.gov (United States)

    Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.

    2012-04-01

    Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.

  20. Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

    Science.gov (United States)

    Gowtham, K. N.; Vasudevan, M.; Maduraimuthu, V.; Jayakumar, T.

    2011-04-01

    Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally, tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.

  1. Gestão da inovação de produto: proposição de um modelo integrado

    Directory of Open Access Journals (Sweden)

    Juliano Pavanelli Stefanovitz

    2014-06-01

    Full Text Available Este trabalho tem por objetivo estabelecer um panorama teórico da gestão do processo de inovação de produto e de seus desafios. Apesar de a importância da inovação ser amplamente reconhecida, há consenso de que as práticas de gestão que a potencializam permanecem ainda não totalmente desvendadas. Vários autores já comprovaram a complexidade do processo de inovação ao mostrar que ele envolve uma intrincada composição de risco e incerteza, necessidade de intensa orquestração multifuncional e a busca permanente de sintonia entre criação e disciplina. Reconhece-se, também, que, apesar dos avanços na última década, a literatura ainda carece de pesquisas que agreguem contribuições de diversas áreas em modelos integrados para gestão sistêmica da inovação. Baseado em revisão bibliográfica da literatura atual, é proposto um modelo composto por três dimensões (processos, contexto organizacional e recursos e os desafios da inovação são investigados sob a óptica dessa estruturação teórica. Além da síntese conceitual sugerida, o presente artigo estabelece um referencial teórico que pode ser útil para pesquisas futuras ligadas ao tema.

  2. Development of decision support system for employee selection using Adaptive Neuro Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    ‘Azzam Abdullah

    2018-01-01

    Full Text Available The number of children day care is increasing from year to year. Children day care is categorized as service industry that help parents in caring and educate children. This type of service industry plays a substitute for the family at certain hours, usually during work hours. The common problems in this industry is related to the employee performance. Most of employees have a less understanding about the whole job. Some employees only perform a routine task, i.e. feeding, cleaning and putting the child to sleep. The role in educating children is not performed as well as possible. Therefore, the employee selection is an important process to solve a children day care problem. An effective decision support system is required to optimize the employee selection process. Adaptive neuro fuzzy inference system (ANFIS is used to develop the decision support system for employee selection process. The data used to build the system is the historical data of employee selection process in children day care. The data shows the characteristic of job applicant that qualified and not qualified. From that data, the system can perform a learning process and give the right decision. The system is able to provide the right decision with an error of 0,00016249. It means that the decision support system that developed using ANFIS can give the right recommendation for employee selection process.

  3. Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques

    Science.gov (United States)

    Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat

    2017-08-01

    The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.

  4. Nursing and fuzzy logic: an integrative review Enfermería y lógica fuzzy: una revisión de integradora Enfermagem e lógica fuzzy: uma revisão integrativa

    Directory of Open Access Journals (Sweden)

    Rodrigo Jensen

    2011-02-01

    Full Text Available This study conducted an integrative review investigating how fuzzy logic has been used in research with the participation of nurses. The article search was carried out in the CINAHL, EMBASE, SCOPUS, PubMed and Medline databases, with no limitation on time of publication. Articles written in Portuguese, English and Spanish with themes related to nursing and fuzzy logic with the authorship or participation of nurses were included. The final sample included 21 articles from eight countries. For the purpose of analysis, the articles were distributed into categories: theory, method and model. In nursing, fuzzy logic has significantly contributed to the understanding of subjects related to: imprecision or the need of an expert; as a research method; and in the development of models or decision support systems and hard technologies. The use of fuzzy logic in nursing has shown great potential and represents a vast field for research.Este estudio tuvo como objetivo realizar una revisión integradora investigando como la lógica fuzzy ha sido utilizada en investigaciones con participación de enfermeros. La búsqueda de los artículos fue realizada en las bases de datos CINAHL, Embase, SCOPUS, Medline y PubMed, sin especificar un intervalo de años determinado. Fueron incluidos artículos en los idiomas: portugués, inglés y castellano; con una temática relacionada a la enfermería y a la lógica fuzzy; y con autoría o participación de enfermeros. La muestra final fue de 21 artículos, de ocho países. Para el análisis, los artículos fueron distribuidos en las categorías: teoría, método y modelo. En la enfermería, la lógica fuzzy ha contribuido significativamente para la comprensión de temas relativos a la imprecisión o a la necesidad del especialista, como método de investigación y en el desarrollo de modelos o sistemas de apoyo a la decisión y de tecnologías duras. El uso de la lógica fuzzy en la enfermería ha demostrado gran

  5. Deteksi Jarak Lokasi Gangguan Pada Saluran Transmisi 500 Kv Cilegon Baru - Cibinong Menggunakan Adaptive Neuro Fuzzy Inference System (ANFIS

    Directory of Open Access Journals (Sweden)

    Muhamad Otong

    2017-06-01

    Full Text Available Pada saluran transmisi diperlukan metode deteksi lokasi gangguan yang akurat dan cepat untuk mengurangi waktu pencarian, sehingga mempercepat proses perbaikan. Dengan menggunakan kombinasi metode Transformasi Park dan Adaptive Neuro Fuzzy Inference System (ANFIS, dapat dideteksi jarak lokasi gangguan secara langsung setelah terjadinya gangguan dengan cara menganalisa gelombang berjalan pada saluran transmisi. Saat terjadi gangguan, akan menyebabkan timbulnya gelombang berjalan yang berupa tegangan dan arus. Tegangan dan arus ini akan ditransformasikan oleh transformasi park pada kedua ujung saluran untuk mendapatkan waktu kedatangan gelombang berjalan, yang mana terdapat perbedaan waktu pada tiap ujung saluran dikarenakan adanya perbedaan jarak yang ada. Perbedaan waktu ini akan di input kedalam ANFIS untuk mendapatkan jarak lokasi gangguan. Dengan membandingkan jumlah nilai keanggotaan dan pemilihan input, maka diperoleh desain ANFIS terbaik adalah dengan jumlah nilai keanggotaan (MF 5 serta input perbedaan waktu ∆tV dan ∆tI (V dan I dengan nilai Mean Absolute Error (MAE sebesar 1,33.

  6. Avaliação de um modelo para a estimativa da lâmina máxima de escoamento superficial

    Directory of Open Access Journals (Sweden)

    N. P. Griebeler

    2001-06-01

    Full Text Available A erosão hídrica é um dos principais processos associados à degradação ambiental, sendo a desagregação e o transporte das partículas do solo decorrentes, principalmente, da energia cinética proveniente do impacto das gotas da chuva sobre o solo e do escoamento superficial. Tendo em vista as perdas na produção agrícola resultantes da erosão hídrica, aliadas ao alto custo de adoção e manutenção de sistemas de conservação de solos, é fundamental que estes sejam instalados de maneira adequada. Visando otimizar o projeto e adotar sistemas de conservação de solos, Pruski e colaboradores desenvolveram um modelo com vistas em obter a lâmina máxima de escoamento superficial em condições típicas de áreas agrícolas. Neste trabalho, procedeu-se à avaliação deste modelo pela comparação dos valores calculados com os obtidos experimentalmente, tendo-se evidenciado pequenas diferenças percentuais e altos coeficientes de correlação entre os valores obtidos experimentalmente e os calculados. Esses valores indicam que o modelo avaliado mostra-se eficiente para prever a lâmina máxima de escoamento superficial.

  7. Resolução de um modelo de reator de leito fixo não adiabático com dispersão axial utilizando redes neurais artificiais - DOI: 10.4025/actascitechnol.v25i1.2238

    Directory of Open Access Journals (Sweden)

    Luiz Henry Monken e Silva

    2003-04-01

    Full Text Available As capacidades de interpolação de redes perceptron multicamada (MLP foram utilizadas para resolver um sistema de equações diferencias ordinárias que modela um reator não-adiabático com leito fixo e dispersão axial. As metodologias descritas neste artigo seguem as propostas por Lagaris et al. (1998, 2000, estendidas para modelos com condições de contorno mistas e pelo uso do método da penalidade para converter o problema de otimização original de restrito para irrestrito no treinamento das redes MLP. Os resultados são compatíveis com aqueles apresentados em Luize e Biscaia (1991, que foram obtidos com técnicas numéricas já consagradas, como elementos finitos e colocação ortogonal. O método de neuro-interpolação adotado neste artigo é de fácil manuseio se comparado com os métodos clássicos para solução numérica de equações diferenciais, particularmente para sistemas diferenciais não-lineares, e define uma aproximação global, na forma analítica, para a solução de problemas.

  8. A classe dos polinômios bivariados de Fibonacci (PBF: elementos recentes sobre a evolução de um modelo.

    Directory of Open Access Journals (Sweden)

    Francisco Regis Vieira Alves

    2017-02-01

    Full Text Available Se constata na abordagem, por parte dos autores de livros de História da Matemática – HM, uma apreciação lacônica, lúdica e desprovida de um vigor histórico-matemático que proporcione ao leitor um entendimento do processo evolutivo irrefreável hodierno do modelo de Fibonacci, originariamente correspondente ao processo biológico de produção de pares de coelhos. A partir dessa perspectiva, o trabalho atual discute a classe dos Polinômios Bivariados Complexos de Fibonacci – PBCF. Os mesmos constituem uma representação generalizada da Sequência de Fibonacci, em termos de uma variável real ‘x’ e a unidade imaginária ‘i’. Assim, o texto aborda e pormenoriza determinados resultados matemáticos que envidam uma perspectiva correlata ao processo ininterrupto evolutivo do modelo de Fibonacci, costumeiramente negligenciados por autores de livros.

  9. Jogo da Minoria: um modelo baseado em agentes aplicado ao mercado financeiro Minority Game: an agent-based model applied to financial market

    Directory of Open Access Journals (Sweden)

    Antonio Fernando Crepaldi

    2012-12-01

    Full Text Available Nos últimos anos houve uma contribuição significativa dos físicos para a construção de um tipo de modelo baseado em agentes que busca reproduzir, em simulação computacional, o comportamento do mercado financeiro. Esse modelo, chamado Jogo da Minoria consiste de um grupo de agentes que vão ao mercado comprar ou vender ativos. Eles tomam decisões com base em estratégias e, por meio delas, os agentes estabelecem um intrincado jogo de competição e coordenação pela distribuição da riqueza. O modelo tem demonstrado resultados bastante ricos e surpreendentes, tanto na dinâmica do sistema como na capacidade de reproduzir características estatísticas e comportamentais do mercado financeiro. Neste artigo, são apresentadas a estrutura e a dinâmica do Jogo da Minoria, bem como as contribuições recentes relacionadas ao Jogo da Minoria denominado de Grande Canônico, que é um modelo mais bem ajustado às características do mercado financeiro e reproduz as regularidades estatísticas do preço dos ativos chamadas fatos estilizados.Over the past ten years physicists have made a significant contribution to the building of an agent-based model to reproduce the behavior of financial markets using computer simulation. This model, called the Minority Game, consists of a group of agents that buy or sell assets. They make decisions based on strategies, and through them the agents establish an intricate game of competition and coordination resulting in the distribution of wealth. The model has shown outstanding surprising results concerning both the dynamics of the system and the ability to reproduce statistical and behavior characteristics of the financial market. In this study, the structure and dynamics of the Minority Game and the recent contributions related to the Grand Canonical Minority game, a model which is better adapted to the characteristics of the financial market and reproduce the statistical regularities of asset prices (called

  10. Avaliação experimental de um modelo numérico para o processo de redistribuição da água no solo

    Directory of Open Access Journals (Sweden)

    P. C. Poliseli

    1999-06-01

    Full Text Available Para testar o desempenho de um modelo numérico em predizer a variação em umidade (θ e tensão da água (|ψm| no tempo e no espaço, foram escolhidos dados da literatura de dois materiais porosos com diferentes propriedades hidráulicas: uma areia marinha (Tottori, Japão e um Latossolo Vermelho-Amarelo de textura média (Piracicaba, SP. Os resultados encontrados levaram às seguintes conclusões: (a em ambos os materiais porosos estudados, o desempenho do modelo foi altamente significativo, onde os perfis de umidade transladaram-se satisfatoriamente no tempo; (b o modelo também foi capaz de prever muito bem o comportamento da densidade de fluxo em função do tempo; (c os maiores desvios do modelo em relação aos dados de campo foram encontrados nos tempos iniciais do processo de redistribuição da água, muito embora esses desvios tenham ocorrido em apenas 0,2% do tempo total estudado no experimento em areia marinha e 2,0% para o Latossolo; (d o desempenho do modelo foi ligeiramente superior para a areia marinha em relação ao Latossolo, devido, provavelmente, à maior homogeneidade nas propriedades hidráulicas da areia. Este trabalho foi realizado, no segundo semestre de 1996, na Universidade Federal do Paraná.

  11. Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

    Science.gov (United States)

    Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng

    2016-05-01

    Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

  12. Estratégia nos fundos de pensão do Brasil: contribuições para a construção de um modelo de análise.

    Directory of Open Access Journals (Sweden)

    José Antônio Valle Antunes Júnior

    2008-02-01

    Full Text Available Em relação aos fundos de pensão, existem poucos estudos relativos à estratégia. Este trabalho contribui para a construção da estratégia nos fundos de pensão no Brasil. Utilizando uma pesquisa exploratória baseada em estudo de caso múltiplo, o objetivo do artigo é propor um modelo orientador para a análise das estratégias dos fundos. Com base em conceitos de competitividade, concorrência e vantagem competitiva, desenvolveu-se um modelo teórico. Em virtude das características específicas desse setor, foram adicionados os conceitos de governança e responsabilidade social. Com base no modelo proposto, elencaram-se os fatores relevantes à formulação de estratégias e as vantagens competitivas para cada um dos casos estudados, tendo sido sugerido um padrão de concorrência de caráter dinâmico para o setor. Propôs-se também uma classificação dos fundos estudados, de acordo com o padrão concebido, e foi possível verificar que os fundos de pensão possuem diferentes percepções sobre o tema concorrência, conforme distintas orientações estratégicas gerais.

  13. Construindo um modelo de sistema de cuidados Construyendo un modelo de sistema de cuidados Developing an explanatory theoretical model of system of care

    Directory of Open Access Journals (Sweden)

    Alacoque Lorenzini Erdmann

    2007-06-01

    Full Text Available OBJETIVO: Construir um modelo teórico explicativo de Sistema de Cuidados. METODO: Estudo exploratório qualitativo apoiado nos pressupostos da Grounded Theory. Participaram do estudo 15 profissionais e os dados foram coletados por entrevista aberta. RESULTADOS: O Modelo teórico foi delimitado a partir do fenômeno Construindo o Sistema Complexo de Cuidados determinado pelas categorias: Convivendo com a dinamicidade do sistema de cuidados e Organizando o sistema de cuidados a partir de múltiplas interações. CONCLUSÃO: O modelo de sistema de cuidados apresentou-se como sistema vital e dinâmico construído a partir de redes interacionais de vários saberes do agir humano expresso pelo trabalho compartilhado, multidisciplinar e em equipe.OBJETIVO: Construir un modelo teórico explicativo de Sistema de Cuidados. MÉTODO: Se trata de un estudio cualitativo exploratorio apoyado en las premisas de la Teoría Fundamentada en los Datos. Participaron del estudio 15 profesionales. Los datos fueron recolectados por medio de una entrevista abierta. RESULTADOS: El modelo teórico se delimitó al fenómeno Construyendo el Sistema Complejo de Cuidados determinado por las categorías: Viviendo junto con la dinamicidad del sistema de cuidados y Organizando el sistema de cuidados a partir de interacciones múltiples. CONCLUSIÓN: El modelo de sistema de cuidados se presenta como un sistema vital y dinámico construido a partir de redes de interacción de conocimientos del actuar humano expresado en el trabajo conjunto, multidisciplinario y en equipo.OBJECTIVE: To develop an explanatory theoretical model of the system of care. METHOD: Grounded theory served as the conceptual framework to conduct this qualitative exploratory study. Unstructured interviews were conducted among 15 care professionals. RESULTS: The theoretical model was delimited from the phenomenon "building complex system of care" as determined by the following categories: "dealing with a

  14. Principais desafios para implantação de um modelo de planejamento e controle do processo de pré-montagem

    OpenAIRE

    Wu, Lincoln

    2007-01-01

    Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-graduação em Engenharia Civil A pesquisa descreve e analisa a questão dos desafios para implantação de um modelo de controle da produção das carrocerias de veículos de passeio e utilitários fabricados na Fiat Automóveis S/A que ainda não estão no processo de montagem. O modelo deve atender os requisitos exigidos pelo processo de acúmulo de pré-montagem, no que tange à interface de sistemas,...

  15. A mathematical theory of shape and neuro-fuzzy methodology-based diagnostic analysis: a comparative study on early detection and treatment planning of brain cancer.

    Science.gov (United States)

    Kar, Subrata; Majumder, D Dutta

    2017-08-01

    Investigation of brain cancer can detect the abnormal growth of tissue in the brain using computed tomography (CT) scans and magnetic resonance (MR) images of patients. The proposed method classifies brain cancer on shape-based feature extraction as either benign or malignant. The authors used input variables such as shape distance (SD) and shape similarity measure (SSM) in fuzzy tools, and used fuzzy rules to evaluate the risk status as an output variable. We presented a classifier neural network system (NNS), namely Levenberg-Marquardt (LM), which is a feed-forward back-propagation learning algorithm used to train the NN for the status of brain cancer, if any, and which achieved satisfactory performance with 100% accuracy. The proposed methodology is divided into three phases. First, we find the region of interest (ROI) in the brain to detect the tumors using CT and MR images. Second, we extract the shape-based features, like SD and SSM, and grade the brain tumors as benign or malignant with the concept of SD function and SSM as shape-based parameters. Third, we classify the brain cancers using neuro-fuzzy tools. In this experiment, we used a 16-sample database with SSM (μ) values and classified the benignancy or malignancy of the brain tumor lesions using the neuro-fuzzy system (NFS). We have developed a fuzzy expert system (FES) and NFS for early detection of brain cancer from CT and MR images. In this experiment, shape-based features, such as SD and SSM, were extracted from the ROI of brain tumor lesions. These shape-based features were considered as input variables and, using fuzzy rules, we were able to evaluate brain cancer risk values for each case. We used an NNS with LM, a feed-forward back-propagation learning algorithm, as a classifier for the diagnosis of brain cancer and achieved satisfactory performance with 100% accuracy. The proposed network was trained with MR image datasets of 16 cases. The 16 cases were fed to the ANN with 2 input neurons, one

  16. Modelo artesanal para treinamento de acesso vascular periférico

    Directory of Open Access Journals (Sweden)

    Ingrid Rodrigues de Oliveira Rocha

    2017-08-01

    Full Text Available Resumo Contexto O acesso vascular é o procedimento mais comum realizado entre pacientes hospitalizados. Assim, na tentativa de minimizar complicações e aliar conhecimento técnico ao conhecimento teórico, os modelos de simulação são capazes de oferecer um ambiente seguro para profissionais em formação e evitar os dilemas éticos de treinamento direto em pacientes. Com esse objetivo, surgiram diversos manequins de treinamento, mas devido ao seu alto custo eles não são acessíveis a todos, e com frequência os profissionais em formação da área da saúde realizam procedimentos sem que tenham um treinamento prévio. Objetivo Desenvolver um modelo de ensino e treinamento de acesso vascular periférico, utilizando um modelo de baixo custo para fins educacionais. Método Para reproduzir a via periférica de acesso, utilizou-se um macarrão de polietileno com equipos de infusão, com uma extremidade em fundo cego e a outra conectada a duas bolsas de 500 mL de soro fisiológico acrescido de corante. A bolsa foi instalada em um suporte metálico. Resultado O formato sugerido para o modelo apresentou semelhança com a anatomia do antebraço simplificada. O modelo se mostrou prático na punção e, devido à sua extensão, tem-se a possibilidade de puncionar diversas vezes o mesmo modelo, facilitando o treinamento. Conclusão O modelo proposto permite o treinamento de acesso vascular periférico, sendo uma alternativa de baixo custo que pode ser utilizada para fins educacionais.

  17. Neuro-fuzzy decoding of sensory information from ensembles of simultaneously recorded dorsal root ganglion neurons for functional electrical stimulation applications

    Science.gov (United States)

    Rigosa, J.; Weber, D. J.; Prochazka, A.; Stein, R. B.; Micera, S.

    2011-08-01

    Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.

  18. Neuro-fuzzy decoding of sensory information from ensembles of simultaneously recorded dorsal root ganglion neurons for functional electrical stimulation applications.

    Science.gov (United States)

    Rigosa, J; Weber, D J; Prochazka, A; Stein, R B; Micera, S

    2011-08-01

    Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.

  19. Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

    Directory of Open Access Journals (Sweden)

    P. Bhattacharya

    2007-11-01

    Full Text Available To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i casual or contextual feature, (ii contact feature, (iii contactless feature, and (iv performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA, is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue. We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.

  20. Modelling a ground-coupled heat pump system using adaptive neuro-fuzzy inference systems

    Energy Technology Data Exchange (ETDEWEB)

    Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

    2008-01-15

    The aim of this study is to demonstrate the usefulness of an adaptive neuro-fuzzy inference system (ANFIS) for the modelling of ground-coupled heat pump (GCHP) system. The GCHP system connected to a test room with 16.24 m{sup 2} floor area in Firat University, Elazig (38.41 N, 39.14 E), Turkey, was designed and constructed. The heating and cooling loads of the test room were 2.5 and 3.1 kW at design conditions, respectively. The system was commissioned in November 2002 and the performance tests have been carried out since then. The average performance coefficients of the system (COPS) for horizontal ground heat exchanger (GHE) in the different trenches, at 1 and 2 m depths, were obtained to be 2.92 and 3.2, respectively. Experimental performances were performed to verify the results from the ANFIS approach. In order to achieve the optimal result, several computer simulations have been carried out with different membership functions and various number of membership functions. The most suitable membership function and number of membership functions are found as Gauss and 2, respectively. For this number level, after the training, it is found that root-mean squared (RMS) value is 0.0047, and absolute fraction of variance (R{sup 2}) value is 0.9999 and coefficient of variation in percent (cov) value is 0.1363. This paper shows that the values predicted with the ANFIS, especially with the hybrid learning algorithm, can be used to predict the performance of the GCHP system quite accurately. (author)

  1. Adaptive neuro-fuzzy inference systems (ANFIS) application to investigate potential use of natural ventilation in new building designs in Turkey

    Energy Technology Data Exchange (ETDEWEB)

    Ayata, Tahir; Cam, Ertugrul; Yildiz, Osman [Kirikkale University, Faculty of Engineering, 71451, Campus, Kirikkale (Turkey)

    2007-05-15

    Natural ventilation in living and working places provides both circulation of clear air and a decrease of indoor temperature, especially during hot summer days. In addition to openings, the dimension ratio and position of buildings play a significant role to obtain a uniform indoor air velocity distribution. In this study, the potential use of natural ventilation as a passive cooling system in new building designs in Kayseri, a midsize city in Turkey, was investigated. First, indoor air velocity distributions with respect to changing wind direction and magnitude were simulated by the FLUENT package program, which employs finite element methods. Then, an adaptive neuro-fuzzy inference systems (ANFIS) model was employed to predict indoor average and maximum air velocities using the simulated data by FLUENT. The simulation results suggest that natural ventilation can be used to provide a thermally comfortable indoor environment during the summer season in the study area. Also, the ANFIS model can be proposed for estimation of indoor air velocity values in such studies. (author)

  2. Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS)

    Science.gov (United States)

    Arozi, Moh; Putri, Farika T.; Ariyanto, Mochammad; Khusnul Ari, M.; Munadi, Setiawan, Joga D.

    2017-01-01

    People with disabilities are increasing from year to year either due to congenital factors, sickness, accident factors and war. One form of disability is the case of interruptions of hand function. The condition requires and encourages the search for solutions in the form of creating an artificial hand with the ability as a human hand. The development of science in the field of neuroscience currently allows the use of electromyography (EMG) to control the motion of artificial prosthetic hand into the necessary use of EMG as an input signal to control artificial prosthetic hand. This study is the beginning of a significant research planned in the development of artificial prosthetic hand with EMG signal input. This initial research focused on the study of EMG signal recognition. Preliminary results show that the EMG signal recognition using combined discrete wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS) produces accuracy 98.3 % for training and 98.51% for testing. Thus the results can be used as an input signal for Simulink block diagram of a prosthetic hand that will be developed on next study. The research will proceed with the construction of artificial prosthetic hand along with Simulink program controlling and integrating everything into one system.

  3. Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS).

    Science.gov (United States)

    Hosseini, Seyed Abolfazl; Esmaili Paeen Afrakoti, Iman

    2018-01-17

    The purpose of the present study was to reconstruct the energy spectrum of a poly-energetic neutron source using an algorithm developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is a kind of artificial neural network based on the Takagi-Sugeno fuzzy inference system. The ANFIS algorithm uses the advantages of both fuzzy inference systems and artificial neural networks to improve the effectiveness of algorithms in various applications such as modeling, control and classification. The neutron pulse height distributions used as input data in the training procedure for the ANFIS algorithm were obtained from the simulations performed by MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology). Taking into account the normalization condition of each energy spectrum, 4300 neutron energy spectra were generated randomly. (The value in each bin was generated randomly, and finally a normalization of each generated energy spectrum was performed). The randomly generated neutron energy spectra were considered as output data of the developed ANFIS computational code in the training step. To calculate the neutron energy spectrum using conventional methods, an inverse problem with an approximately singular response matrix (with the determinant of the matrix close to zero) should be solved. The solution of the inverse problem using the conventional methods unfold neutron energy spectrum with low accuracy. Application of the iterative algorithms in the solution of such a problem, or utilizing the intelligent algorithms (in which there is no need to solve the problem), is usually preferred for unfolding of the energy spectrum. Therefore, the main reason for development of intelligent algorithms like ANFIS for unfolding of neutron energy spectra is to avoid solving the inverse problem. In the present study, the unfolded neutron energy spectra of 252Cf and 241Am-9Be neutron sources using the developed computational code were

  4. Impacto da geometria do cânion urbano na intensidade de ilha de calor noturna: análise através de um modelo simplificado adaptado a um SIG

    Directory of Open Access Journals (Sweden)

    Camila Mayumi Nakata-Osaki

    Full Text Available Resumo A geometria urbana é um dos fatores de maior influência na intensidade da ilha de calor urbana. Seu estudo requer a caracterização de cânions urbanos, geralmente medidos pela relação entre a altura dos edifícios e a largura da rua (H/W, conceito aplicado no modelo numérico de Oke em 1981. O objetivo deste artigo é verificar o impacto da geometria do cânion urbano na intensidade de ilhas de calor noturna. Para isso, foram realizados levantamento de dados climáticos e de geometria urbana em duas cidades brasileiras. Os valores de intensidade de ilha de calor foram confrontados com os simulados pelo modelo original de Oke (1981, o qual foi calibrado e adaptado à plataforma SIG, de forma a possibilitar a incorporação de outro parâmetro de geometria, além da relação H/W: o comprimento de rugosidade. Esse processo gerou uma nova ferramenta de cálculo, que é denominda THIS (Tool for Heat Island Simulation. Aplicou-se o novo modelo para simular alguns cenários urbanos hipotéticos, que representam vários tipos de cânions urbanos. Os resultados demonstraram que cânions urbanos de maior rugosidade amenizam as intensidades de ilha de calor noturna em relação a um cânion de mesmo valor de relação H/W e menor rugosidade.

  5. Artificial neural networks and neuro-fuzzy inference systems as virtual sensors for hydrogen safety prediction

    Energy Technology Data Exchange (ETDEWEB)

    Karri, Vishy; Ho, Tien [School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, Tasmania 7001 (Australia); Madsen, Ole [Department of Production, Aalborg University, Fibigerstraede 16, DK-9220 Aalborg (Denmark)

    2008-06-15

    Hydrogen is increasingly investigated as an alternative fuel to petroleum products in running internal combustion engines and as powering remote area power systems using generators. The safety issues related to hydrogen gas are further exasperated by expensive instrumentation required to measure the percentage of explosive limits, flow rates and production pressure. This paper investigates the use of model based virtual sensors (rather than expensive physical sensors) in connection with hydrogen production with a Hogen 20 electrolyzer system. The virtual sensors are used to predict relevant hydrogen safety parameters, such as the percentage of lower explosive limit, hydrogen pressure and hydrogen flow rate as a function of different input conditions of power supplied (voltage and current), the feed of de-ionized water and Hogen 20 electrolyzer system parameters. The virtual sensors are developed by means of the application of various Artificial Intelligent techniques. To train and appraise the neural network models as virtual sensors, the Hogen 20 electrolyzer is instrumented with necessary sensors to gather experimental data which together with MATLAB neural networks toolbox and tailor made adaptive neuro-fuzzy inference systems (ANFIS) were used as predictive tools to estimate hydrogen safety parameters. It was shown that using the neural networks hydrogen safety parameters were predicted to less than 3% of percentage average root mean square error. The most accurate prediction was achieved by using ANFIS. (author)

  6. Cícero Rafael Barros Utilização de um modelo estocástico para mensuração do passivo atuarial de fundos de pensão

    OpenAIRE

    Dias, Cícero Rafael Barros

    2008-01-01

    Este estudo tem por objetivo apresentar uma análise de um modelo estocástico para a mensuração do passivo atuarial de um Fundo de Pensão, através da Simulação de Monte Carlo, comparar com o método determinístico de avaliação atuarial, bem como analisar a sensibilidade das reservas matemáticas em relação à alteração na tábua de mortalidade utilizada nos cálculos. Para tanto, são consideradas como principais variáveis do modelo as taxas de mortalidade por idade, identificada como...

  7. Inflação inercial como um processo de longa memória: análise a partir de um modelo Arfima-Figarch

    Directory of Open Access Journals (Sweden)

    Erik Alencar de Figueiredo

    2009-06-01

    Full Text Available O objetivo principal deste estudo é investigar a dependência de longo prazo da inflação brasileira, descrevendo-a como um processo fracionariamente integrado tanto na média quanto na variância. A metodologia empregada baseia-se na estimação de um modelo ARFIMA-FIGARCH, capaz de detectar a presença de memória longa em altas defasagens de um processo autorregressivo. Os principais resultados alcançados indicam que, para o período pós-Plano Real, a inflação brasileira exibe um comportamento estacionário em seus dois primeiros momentos com lento decaimento hiperbólico. Há indícios de longa memória na média e na variância do processo. Além disso, constatou-se, para esse período, uma recíproca influência entre a volatilidade e a taxa média de inflação.The aim of this paper is search for the long memory in the Brazilian inflation rate, describing it as a fractionally integrated process in the first and second moments. So, it is employed the more recent methodology of ARFIMA-FIGARCH models. The main result endorses the hypothesis of inertial inflation in the short and long run, and the Friedman's hypothesis of interaction between mean and volatility of price inflation.

  8. Validação de um Modelo Conceitual de Experiência de Compra Online para Consumidores Brasileiros

    Directory of Open Access Journals (Sweden)

    Larissa Soares de Queiroz

    2016-12-01

    Full Text Available O presente estudo objetiva validar o Modelo Conceitual de Experiência de Compra Online (Rose et al., 2012, que mensura a experiência de compra em ambientes digitais, dentro da realidade do comércio eletrônico nacional. Para tanto foi abordado referencial teórico sobre Online Customer Experience (OCE, com breve contextualização do modelo estrutural e dos critérios de validação para o mesmo. A metodologia de pesquisa baseou-se em uma amostra estimada por mínimos quadrados parciais (PLS de 220 consumidores online. Para a coleta de dados, utilizou-se um questionário com 61 itens referentes às variáveis latentes que compõem o modelo. Os resultados obtidos foram submetidos à Análise Fatorial Confirmatória (AFC para validação da estrutura, alcançando resultados satisfatórios nos critérios de confiabilidade, (cargas fatoriais superiores a 0,7 para alfa de Cronbach e Confiabilidade Composta, validade convergente (cargas entre 0,53 e 1,00 e validade discriminante (com √VME superior as correlações em todas as variáveis latentes. Deste modo, os resultados indicam que a estrutura foi validada para consumidores brasileiros, agregando relevância à proposta de mensuração da OCE na realidade do Brasil através do modelo utilizado. 

  9. Short-term load forecasting by a neuro-fuzzy based approach

    Energy Technology Data Exchange (ETDEWEB)

    Ruey-Hsun Liang; Ching-Chi Cheng [National Yunlin University of Science and Technology (China). Dept. of Electrical Engineering

    2002-02-01

    An approach based on an artificial neural network (ANN) combined with a fuzzy system is proposed for short-term load forecasting. This approach was developed in order to reach the desired short-term load forecasting in an efficient manner. Over the past few years, ANNs have attained the ability to manage a great deal of system complexity and are now being proposed as powerful computational tools. In order to select the appropriate load as the input for the desired forecasting, the Pearson analysis method is first applied to choose two historical record load patterns that are similar to the forecasted load pattern. These two load patterns and the required weather parameters are then fuzzified and input into a neural network for training or testing the network. The back-propagation (BP) neural network is applied to determine the preliminary forecasted load. In addition, the rule base for the fuzzy inference machine contains important linguistic membership function terms with knowledge in the form of fuzzy IF-THEN rules. This produces the load correction inference from the historical information and past forecasted load errors to obtain an inferred load error. Adding the inferred load error to the preliminary forecasted load, we can obtain the finial forecasted load. The effectiveness of the proposed approach to the short-term load-forecasting problem is demonstrated using practical data from the Taiwan Power Company (TPC). (Author)

  10. Proposta de um modelo gerencial com ênfase na qualidade para micro e pequenas empresas de confecção

    Directory of Open Access Journals (Sweden)

    Leoni Pentiado Godoy

    2015-04-01

    Full Text Available A transição do século XX, para XXI, veio acompanhada de intensas e rápidas transformações sociais, econômicas e culturais. No ambiente organizacional, este cenário tem se tornado um desafio para as lideranças das organizações. A presente pesquisa tem como objetivo propor um Modelo Gerencial da Qualidade o qual possa auxiliar os administradores de micro e pequenas empresas (MPEs de confecção. A fim de assegurar a melhoria contínua do processo produtivo com base no perfil liderança. As MPEs objeto deste estudo foram em número de seis (6, localizadas no município de Toledo/Paraná. Os dados foram levantados através da aplicação de questionário. Pela análise dos dados evidencia-se o perfil dos gerentes, quanto à liderança. Foi sugerido um modelo para ajudar, às MPEs de confecções, a resolverem os seus problemas de forma adequada. Especialmente a partir da percepção dos colaboradores, proporcionando, uma constante melhoria dos processos e padrão de qualidade das confecções. Portanto, o modelo por meio de adaptações pertinentes um gerenciamento eficaz, com uma proposta de gestão com perspectiva de melhoria contínua, das MPEs de confecções. The transition from the twentieth century to the twenty-first, was accompanied by intense and rapid social, economic and cultural transformations. In the organizational setting, this scenario has become a challenge for the leadership of the organizations. The present paper aimed to propose a model of quality management that may assist administrators of micro and small companies company (SMEs clothing. To ensure continuous improvement of the production process based on the profile leadership. The SMEs object of this study were six in number (6, located in the city of Toledo/Paraná – Brazil. Data were collected through a questionnaire. Data analysis shows the profile of managers regarding leadership. Suggested a model to help, to SMEs clothing, to solve its problems

  11. Cognición imitativa para un robot mediante una comunidad de replicadores neuro-meméticos

    Directory of Open Access Journals (Sweden)

    Dante Giovanni Sterpin Buitrago

    2015-04-01

    Full Text Available Como herramienta de inteligencia artificial, la computación memética emplea modelos de ciertos elementos cerebrales, llamados neuro-memes, hipotéticamente implicados en la simbolización, diseminación y evolución de las características culturales en las sociedades humanas. Con la finalidad de simular el gran potencial evolutivo de los neuro-memes, recientemente se presentó el Perceptrón auto-supervisado (SSP como replicador neuro-memético artificial, cuya capacidad de aprendizaje imitativo fue verificada al controlar un robot muy sencillo. Con respecto a dicha capacidad se observó una deficiencia aprehensiva cuando el imitador solamente observa la conducta libremente evidenciada por el instructor. Al atender dicha deficiencia se encontró la incapacidad de imitar una conducta con ciertas características. Considerando que el SSP es un modelo orientado al desarrollo de sistemas colectivos, en este artículo se presenta un sistema multi-agente compuesto por varios SSP, con el cual se solucionaron dichas dificultades encontradas en un SSP individual.

  12. Aplicação do modelo hipercubo com prioridade na fila com mais de um servidor preferencial sem considerar a hipótese de backup parcial: estudo de caso em um SAMU

    Directory of Open Access Journals (Sweden)

    Caio Vitor Beojone

    Full Text Available Resumo O estudo de Sistemas de Atendimento Emergencial – SAE visa encontrar meios de fornecer serviços de saúde efetivos e melhorar a qualidade de vida da população respeitando as limitações de recursos disponíveis. Nesse contexto, o objetivo do presente trabalho foi mostrar o potencial de aplicação do modelo hipercubo com prioridade na fila com mais de um servidor preferencial sem considerar a hipótese de backup parcial em Sistemas de Atendimento Móvel de Urgência – SAMU em que o nível de utilização do sistema é relativamente baixo. Para isso foram realizados alguns experimentos do modelo hipercubo com prioridade na fila sem backup parcial e prospecção de cenários futuros por meio de um estudo de caso no SAMU da cidade de Bauru, SP. Foram avaliados os impactos do aumento na demanda sobre o sistema e o quanto e como (aonde localizar? a aquisição de uma nova ambulância pode melhorar as medidas de desempenho do sistema. Os principais resultados mostram que um aumento de 50% na demanda pode dobrar o tempo de resposta dessas ambulâncias, por outro lado, aumentos mais discretos têm um impacto pequeno sobre o sistema, como pode ser visto nos aumentos de 5,71% e 13,57%, nos quais o acréscimo nos tempos de resposta foram de 5% e 16%, respectivamente. A aquisição de uma nova ambulância foi avaliada em termos das medidas de desempenho e os melhores resultados em todos os cenários se deu quando ela estava presente no átomo Boulevard, obtendo um tempo médio de resposta 3% inferior às demais localidades, em média.

  13. Clima organizacional: análise fatorial confirmatória de modelos de mensuração concorrentes

    Directory of Open Access Journals (Sweden)

    Daniel Ioshiteru Kinpara

    Full Text Available O objetivo deste trabalho foi verificar qual modelo de mensuração para a Escala Clima Organizacional (ECO ajusta-se melhor aos dados. Quatro modelos concorrentes foram comparados: 1 o modelo de sete fatores de Laros e Puente-Palacios; 2 um modelo de cinco fatores derivado do modelo anterior; 3 um modelo de seis fatores baseado na teoria de campo vital de Lewin; e 4 um modelo de três fatores baseado na teoria de motivação de McClelland. Foram analisados os dados de 9.901 respondentes da ECO. Os resultados de análise fatorial confirmatória indicaram o modelo de três fatores como o melhor. Todos os modelos mostram ajuste suficiente aos dados. Sugere-se que a escolha do modelo baseie-se na finalidade de uso da escala.

  14. Pedagogia de projetos na biblioteca escolar: proposta de um modelo para o processo da pesquisa escolar

    OpenAIRE

    Castro,César Augusto; Sousa,Maria Conceição Pereira de

    2008-01-01

    Mostra a importância da pesquisa no processo de ensino-aprendizagem. Enfoca a biblioteca escolar como estimuladora da prática da pesquisa escolar, a partir da Pedagogia de Projetos. Evidencia resultados de pesquisa de campo em uma escola de São Luís-MA, dirigida a alunos, professores e ao ambiente da biblioteca da escola. Propõe um modelo para o processo de pesquisa, através do uso de projetos, a ser aplicado no âmbito da biblioteca escolar, promovendo a eficácia da construção, concretização ...

  15. Um modelo de provisionamento elástico de recursos baseado em níveis de estresse

    OpenAIRE

    Pagani, Diego Henrique

    2016-01-01

    Orientador : Prof. Dr. Luis Carlos Erpen de Bona Dissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 08/07/2016 Inclui referências : f. 44-48 Àrea de concentração: Ciência da computação Resumo: A computação em nuvem pode ser definida como um modelo de compartilhamento de recursos computacionais,que podem ser adicionados e removidos de forma dinâmica e elástica possibilitando ajustar os recur...

  16. Drought Forecasting Using Adaptive Neuro-Fuzzy Inference Systems (ANFIS, Drought Time Series and Climate Indices For Next Coming Year, (Case Study: Zahedan

    Directory of Open Access Journals (Sweden)

    Hossein Hosseinpour Niknam

    2012-07-01

    Full Text Available In this research in order to forecast drought for the next coming year in Zahedan, using previous Standardized Precipitation Index (SPI data and 19 other climate indices were used.  For this purpose Adaptive Neuro-Fuzzy Inference System (ANFIS was applied to build the predicting model and SPI drought index for drought quantity.  At first calculating correlation approach for analysis between droughts and climate indices was used and the most suitable indices were selected. In the next stage drought prediction for period of 12 months was done. Different combinations among input variables in ANFIS models were entered. SPI drought index was the output of the model.  The results showed that just using time series like the previous year drought SPI index in forecasting the 12 month drought was effective. However among all climate indices that were used, Nino4 showed the most suitable results.

  17. Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

    Directory of Open Access Journals (Sweden)

    Mosbeh R. Kaloop

    2015-10-01

    Full Text Available This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2 the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and (3 The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.

  18. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

    Science.gov (United States)

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

  19. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

    Directory of Open Access Journals (Sweden)

    Changho Jhin

    Full Text Available One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS applied quantitative structure-activity relationship models (QSAR were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

  20. Prediction of matching condition for a microstrip subsystem using artificial neural network and adaptive neuro-fuzzy inference system

    Science.gov (United States)

    Salehi, Mohammad Reza; Noori, Leila; Abiri, Ebrahim

    2016-11-01

    In this paper, a subsystem consisting of a microstrip bandpass filter and a microstrip low noise amplifier (LNA) is designed for WLAN applications. The proposed filter has a small implementation area (49 mm2), small insertion loss (0.08 dB) and wide fractional bandwidth (FBW) (61%). To design the proposed LNA, the compact microstrip cells, an field effect transistor, and only a lumped capacitor are used. It has a low supply voltage and a low return loss (-40 dB) at the operation frequency. The matching condition of the proposed subsystem is predicted using subsystem analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To design the proposed filter, the transmission matrix of the proposed resonator is obtained and analysed. The performance of the proposed ANN and ANFIS models is tested using the numerical data by four performance measures, namely the correlation coefficient (CC), the mean absolute error (MAE), the average percentage error (APE) and the root mean square error (RMSE). The obtained results show that these models are in good agreement with the numerical data, and a small error between the predicted values and numerical solution is obtained.

  1. Alocação de referees para avaliar trabalhos submetidos a um congresso de grande porte: modelo e caso ENEGEP

    Directory of Open Access Journals (Sweden)

    Flavio Cesar F. Fernandes

    2001-06-01

    Full Text Available Este artigo apresenta uma experiência real de implantação de um modelo de Pesquisa Operacional para realizar a alocação de referees para avaliar trabalhos submetidos a um congresso de grande porte. Utilizando os dados reais do XV ENEGEP (Encontro Nacional de Engenharia de Produção/ I ICIE (First International Congress of Industrial Engineering, foi mostrada a grande superioridade do método computadorizado sobre o método real. No congresso seguinte XVI ENEGEP / II ICIE o modelo efetuou a alocação de 403 referees para avaliar 954 trabalhos. O modelo se mostrou totalmente validado e algumas imprecisões que surgiram se deveu a informações não totalmente corretas sobre alguns referees. E também apresentado o uso deste modelo no processo de avaliação de trabalhos ligados à área de Gerência submetidos ao XXI ENEGEP Nas conclusões são feitas algumas considerações que poderão ser incorporadas em futuros congressos no sentido de aprimorar ainda mais o processo de avaliação de trabalhos, que sem dúvida alguma, é um dos processos mais críticos na organização de congressos científicos de grande porte.This paper presents an actual experience in implementing an Operations Research model to allocate referees to assess papers submitted to a large size congress. By means of the actual data from XV ENEGEP (National Congress of Production Engineering, it was demonstrated the great superiority of the computerized method against the method (empirical and manual utilized. In the following congress (XVI ENEGEP III ICIE the model made the allocation of 403 referees to assess 954 papers. The model was absolutely validated and some inaccuracies that occurred were due to the information not absolutely correct about some referees. It is also presented the usage of this model in the XXI ENEGEP's assessment process of papers related to Management area. At the conclusions, it is drawn some considerations that intend contribute to the

  2. Um modelo alternativo para a quantificação de multiprodutos em árvores individuais Alternative model for the quantification of multiproducts in individual trees

    Directory of Open Access Journals (Sweden)

    Thelma Shirlen Soares

    2004-12-01

    Full Text Available Neste estudo foram conduzidos testes preliminares com o objetivo de avaliar um modelo de taper, proposto a partir da adaptação dos modelos desenvolvidos por Ormerod (1973 e Turnbull (1979, caracterizados pela sua simplicidade e facilidade de ajuste. Em comparação com modelos já consagrados, observou-se que o modelo alternativo apresentou consistência nos ajustes realizados. Portanto, pode-se concluir que o modelo apresentado é recomendável para a quantificação de multiprodutos de árvores individuais.Preliminary tests were conducted with the objective of evaluating a taper model, proposed from the adaptation of the models developed by Ormerod (1973 and Turnbull (1979, characterized by the simplicity and adjustment easiness. After adjustments and comparisons with other consecrated models, it was observed that the alternative model presented consistency in the carried out adjustments. In conclusion, the presented model can be recommended for multiproduct quantification of individual trees.

  3. Um estudo comparativo entre os modelos de instalação e consolidação das indústrias aeronáuticas brasileiras e canadenses

    Directory of Open Access Journals (Sweden)

    José Fernando Touguinha de Almeida

    2016-01-01

    Full Text Available No atual cenário econômico mundial, altamente competitivo e globalizado, as indústrias introduziram significativas mudanças nas estruturas organizacionais, principalmente aquelas dependentes de alta tecnologia, remodelando suas estratégias de negócio e redefinindo os modelos de parcerias com a cadeia produtiva e a rede de clientes. Nesse novo contexto econômico, as transferências de tecnologia se transformaram em um importante diferencial competitivo para as indústrias com base tecnológica, permitindo a especialização das empresas nos seus segmentos de atuação. Alguns modelos de transferência de tecnologia foram propostos e adotados mundialmente pelas indústrias, e o setor aeronáutico foi um dos que implementaram esses modelos e que mais evoluíram na busca do modelo colaborativo ideal, formando clusters e arranjos produtivos locais para consolidar e fortalecer os resultados. O objetivo desta pesquisa é discutir os modelos de instalação e consolidação adotados pelas indústrias aeronáuticas canadense e brasileira, identificando suas diferenças e similaridades. Para tanto, a metodologia baseou-se na utilização da abordagem qualitativa e de pesquisa documental. Com essa abordagem foi possível realizar uma análise completa e detalhada dos modelos de instalação e consolidação adotados pelas indústrias aeronáuticas brasileira e canadense. Os resultados apontam que, embora as duas principais empresas montadoras de aeronaves brasileira e canadense atuem no mesmo segmento da aviação regional, a formação das indústrias aeronáuticas brasileira e canadense se deu de maneira diferente, justificando o distinto estágio tecnológico em que se encontram atualmente.

  4. TV universitária, um modelo de gestão em construção: TV UNAERP de Ribeirão Preto

    OpenAIRE

    Martelli, Flávia Cortese [UNESP

    2012-01-01

    Esta pesquisa tem como objetivo traçar o perfil de um grupo de TVs universitárias do Brasil e da TV Unaerp de Ribeirão Preto e analisar os modelos de gestão adotados por elas, já que a maior parte veicula a programação por meio da TV por assinatura via cabo. As características que delineiam as TVs universitárias ainda estão sendo formadas no país, portanto, necessita-se identificar os modelos de negócio e as leis que regulamentaram o setor e, desta maneira, verificar de que forma afetam o des...

  5. Modelo de capacidades e maturidade para defesa cibernética

    OpenAIRE

    Sylvio Andre Diogo Silva

    2011-01-01

    Uma implantação eficaz de defesa cibernética requer o desenvolvimento de esforços coordenados nas seguintes capacidades-chave: detecção de ataques, mecanismos de defesa, monitoramento de situação, comando e controle, aprimoramento de estratégias e táticas e desenvolvimento seguro de sistemas. A dissertação apresenta um modelo de capacidades para defesa cibernética. A partir das seis capacidades-chave, foi identificado um modelo com capacidades essenciais para a defesa cibernética. O modelo de...

  6. Avaliação da eficácia de um antagonista do receptor do peptídeo liberador da gastrina em modelos experimentais de artrite

    OpenAIRE

    Patricia Gnieslaw de Oliveira

    2011-01-01

    O peptídeo liberador da gastrina (GRP) é o homologo mamífero da bombesina (BN). Ambos GRP e seus receptores têm sido encontrados na sinóvia de pacientes com artríte reumatoide. O receptor do peptídeo liberador da gastrina (GRPR) pode ser considerado como um alvo terapêutico para doenças inflamatórias. RC-3095 é um antagonista do receptor de GRP. Este estudo avaliou os efeitos anti-inflamatórios do RC-3095, um antagonista específico do receptor do peptídeo liberador da gastrina, em dois modelo...

  7. Modelo de Gestión de los Recursos Humanos con base en la teoría de los subconjuntos borrosos (Human Resource Management Model based on Fuzzy Subsets Theory

    Directory of Open Access Journals (Sweden)

    Lourdes Souto Anido

    2016-11-01

    Full Text Available Resumen. Los Recursos Humanos constituyen los únicos elementos vivos presentes en todo tipo de organización, quienes, a partir de sus conocimientos, habilidades y motivaciones, impulsan a las empresas a ser competitivas y a alcanzar sus metas. En aras de lograr dicho objetivo se hace necesario contar con herramientas que ayuden a que el proceso de toma de decisiones concerniente a la gestión de los mismos sea lo más certeros y objetivos. El presente trabajo propone un modelo de gestión de recursos humanos que integra herramientas basadas en la teoría de los subconjuntos borrosos. La investigación se encuentra estructurada en tres epígrafes. El primero analiza los modelos utilizados en Cuba para gestionar los recursos humanos. El segundo presenta el Modelo de Gestión de los Recursos Humanos que se propone, para finalmente, en un tercer epígrafe proceder a validarlo. La novedad del trabajo que se propone, radica en la integración de herramientas de la teoría de los subconjuntos borrosos en los procesos claves de la Gestión de los Recursos Humanos. Entre sus aportes se encuentran: la aplicación del método de alisado exponencial borroso a la planeación del personal, la extrapolación de un modelo de asignación resuelto a través del algoritmo húngaro a la formación del personal, así como la construcción de un sistema de indicadores borrosos para la evaluación de la Gestión de los Recursos Humanos. La propuesta fue validada a través de aplicaciones parciales en empresas seleccionadas así como con criterio de expertos. English abstract. Human resources are the only living elements present in all types of organizations who, from their knowledge, skills and motivations driving companies to be competitive and achieve their objectives and goals. In order to achieve this objective it is necessary to have tools that help the decision-making process regarding the management of human resources is as accurate and objective as possible

  8. Integration of Adaptive Neuro-Fuzzy Inference System, Neural Networks and Geostatistical Methods for Fracture Density Modeling

    Directory of Open Access Journals (Sweden)

    Ja’fari A.

    2014-01-01

    Full Text Available Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS and Neural Networks (NN algorithms for overall estimation of fracture density from conventional well log data. A simple averaging method was used to obtain a better result by combining results of ANFIS and NN. The algorithm applied on other wells of the field to obtain fracture density. In order to model the fracture density in the reservoir, we used variography and sequential simulation algorithms like Sequential Indicator Simulation (SIS and Truncated Gaussian Simulation (TGS. The overall algorithm applied to Asmari reservoir one of the SW Iranian oil fields. Histogram analysis applied to control the quality of the obtained models. Results of this study show that for higher number of fracture facies the TGS algorithm works better than SIS but in small number of fracture facies both algorithms provide approximately same results.

  9. Avaliação de um modelo de predição para apneia do sono em pacientes submetidos a polissonografia

    OpenAIRE

    Musman,Silvio; Passos,Valéria Maria de Azeredo; Silva,Izabella Barreto Romualdo; Barreto,Sandhi Maria

    2011-01-01

    OBJETIVO: Testar um modelo de predição para apneia do sono a partir de variáveis sociodemográficas e clínicas em uma população com suspeita de distúrbio do sono e submetida à polissonografia. MÉTODOS: Foram incluídos no estudo 323 pacientes consecutivos submetidos à polissonografia por suspeita clínica de distúrbio do sono. Utilizou-se um questionário com questões sociodemográficas e a escala de sonolência de Epworth. Foram medidos pressão arterial, peso, altura e SpO2. A regressão linear múl...

  10. DESENVOLVIMENTO DE UM MÉTODO MONTE CARLO NÃO PARAMÉTRICO PARA SIMULAR FONTES RADIATIVAS PLANARES EM MODELOS COMPUTACIONAIS DE EXPOSIÇÃO

    Directory of Open Access Journals (Sweden)

    José Wilson Vieira

    2014-07-01

    Full Text Available Nos modelos computacionais de exposição (MCEs do DEN/UFPE, um fantoma (simulador antropomórfico é acoplado ao código Monte Carlo (MC EGSnrc para avaliações dosimétricas. Os modelos também precisam de um simulador da fonte emissora de radiação. Este trabalho apresenta um algoritmo MC não paramétrico de uma fonte radioativa planar, isotrópica por rotação, dependente da variável radial e da função de distribuição acumulada (FDA do problema, conhecida, mas não inversível. O algoritmo escolhe N valores da variável radial e calcula os correspondentes valores da função densidade de probabilidade (FDP e da FDA do problema. A suposição usada é que, se os valores da FDA fossem discretos, corresponderiam a frequências de raio (FR em intervalos consecutivos do domínio. Usando um gerador de números aleatórios (GNA uniformes normalizados em [0, 1, a técnica MC da inversão generalizada e interpolações lineares, foi possível obter amostras de r. Para comparações dosimétricas, foi reutilizado o MCE MSTA (Mash STAnding = fantoma MASH + EGSnrc + algoritmo baseado na FDP exponencial e montado o MSTA_FR. As implementações foram adicionadas ao software MonteCarlo, desenvolvido pelos autores e atualizado sempre que necessário. Os resultados apresentados e comentados estabelecem um novo algoritmo para uma fonte radioativa planar.

  11. FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    Pelin GORGEL

    2013-01-01

    Full Text Available This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT. Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS based fuzzy subtractive clustering and Support Vector Machines (SVM methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.

  12. Modelo de estimação de Brand Equity

    OpenAIRE

    Marta Olívia Rovedder de Oliveira

    2013-01-01

    Embora haja um consenso sobre a importância do valor da marca, um modelo de estimação único e uniformemente aceito ainda não surgiu, nem na perspectiva baseada no cliente ou consumidor nem na perspectiva da firma ou financeira. Assim, os profissionais de marketing ainda são desafiados a estimar o valor da marca. Além disso, é importante para a construção de um modelo de estimativa de valor da marca detectar as percepções dos consumidores e também apresentar uma estimativa monetária do valor d...

  13. Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach

    Science.gov (United States)

    Taufik, Afirah; Sakinah Syed Ahmad, Sharifah

    2016-06-01

    The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.

  14. Desenvolvimento de um modelo de programação linear para o Problema da Construção de Grades Horárias em Universidades

    Directory of Open Access Journals (Sweden)

    Guilherme Brandelli Bucco

    Full Text Available Resumo: A construção de grades horárias dos cursos de uma universidade é um problema que deve ser enfrentado no início de todos os semestres e, por mobilizar quantidades significativas de recursos, se constitui numa importante tarefa administrativa. É classificado, em termos de complexidade computacional, como NP-hard, o que implica grande exigência de capacidade de processamento. É modelado de maneiras muito diversas, no intuito de se obter adequação quanto ao contexto educacional do país, às regras específicas da instituição ou aos objetivos específicos dos gestores, entre outros. Neste artigo, propõe-se um modelo matemático para construir grades de horários, otimizando a utilização de salas de aula. Para resolver o modelo proposto, desenvolveu-se um algoritmo que divide o problema para viabilizar o uso de programação linear inteira mista. Experimentos computacionais aplicados a uma base de dados real de uma universidade pública brasileira confirmaram o bom desempenho da abordagem proposta, reduzindo consideravelmente a quantidade de salas de aulas alocadas.

  15. Proposição de um modelo de analise da competitividade organizacional com base no valor : aplicação no setor de ceramica para revestimento

    OpenAIRE

    Muller, Gisela Regina

    1996-01-01

    Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnologico A partir da revisão das principais abordagens existentes sobre o conceito de competitividade e de alguns dos principais modelos de análise da competitividade, é proposto um modelo de análise da competitividade organizacional com base no conceito de valor. Quando a questão central colocada é como analisar a competitividade empresarial, é necessário uma abordagem refinada, que permita captar as nuances dos fa...

  16. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Salahshoor, Karim [Department of Instrumentation and Automation, Petroleum University of Technology, Tehran (Iran, Islamic Republic of); Kordestani, Mojtaba; Khoshro, Majid S. [Department of Control Engineering, Islamic Azad University South Tehran branch (Iran, Islamic Republic of)

    2010-12-15

    The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults. (author)

  17. Modelos de engajamento

    OpenAIRE

    Santos,Roberval de Jesus Leone dos

    2005-01-01

    O artigo apresenta três modelos de engajamento propostos por autores fundamentais do século XX: Gramsci, Sartre e Camus. Após a revisão dos modelos, o autor desenvolve uma abordagem generalizada do fenômeno e apresenta duas conclusões principais: o engajamento tem de ser entendido em termos de grau; o engajamento é um fenômeno exclusivo de sociedades políticas ou antagônicas.The paper presents three models of engagement proposed by fundamental authors of the 20th century: Gramsci, Sartre and ...

  18. Pedagogia de projetos na biblioteca escolar: proposta de um modelo para o processo da pesquisa escolar

    Directory of Open Access Journals (Sweden)

    César Augusto Castro

    Full Text Available Mostra a importância da pesquisa no processo de ensino-aprendizagem. Enfoca a biblioteca escolar como estimuladora da prática da pesquisa escolar, a partir da Pedagogia de Projetos. Evidencia resultados de pesquisa de campo em uma escola de São Luís-MA, dirigida a alunos, professores e ao ambiente da biblioteca da escola. Propõe um modelo para o processo de pesquisa, através do uso de projetos, a ser aplicado no âmbito da biblioteca escolar, promovendo a eficácia da construção, concretização e internalização da pesquisa.

  19. Simulating maize phenology as a function of air temperature with a linear and a nonlinear model Simulação da fenologia do milho em função da temperatura do ar por um modelo linear e um não linear

    Directory of Open Access Journals (Sweden)

    Nereu Augusto Streck

    2008-04-01

    Full Text Available The objective of this study was to adapt a nonlinear model (Wang and Engel - WE for simulating the phenology of maize (Zea mays L., and to evaluate this model and a linear one (thermal time, in order to predict developmental stages of a field-grown maize variety. A field experiment, during 2005/2006 and 2006/2007 was conducted in Santa Maria, RS, Brazil, in two growing seasons, with seven sowing dates each. Dates of emergence, silking, and physiological maturity of the maize variety BRS Missões were recorded in six replications in each sowing date. Data collected in 2005/2006 growing season were used to estimate the coefficients of the two models, and data collected in the 2006/2007 growing season were used as independent data set for model evaluations. The nonlinear WE model accurately predicted the date of silking and physiological maturity, and had a lower root mean square error (RMSE than the linear (thermal time model. The overall RMSE for silking and physiological maturity was 2.7 and 4.8 days with WE model, and 5.6 and 8.3 days with thermal time model, respectively.O objetivo deste trabalho foi adaptar um modelo não linear (Wang e Engel - WE, para simular a fenologia do milho (Zea mays L., e avaliar esse modelo e um modelo linear (soma térmica, para estimar os estágios de desenvolvimento de uma variedade de milho cultivada em campo. Um experimento de dois anos, com sete datas anuais de semeadura cada ano, foi conduzido em Santa Maria, RS, durante os anos agrícolas 2005/2006 e 2006/2007. Foram registradas as datas de emergência, espigamento e maturação fisiológica da variedade de milho BRS Missões, em seis repetições, em cada data de semeadura. Os dados coletados no ano agrícola 2005/2006 foram usados para estimar os coeficientes dos dois modelos, e os dados coletados no ano agrícola 2006/2007 foram usados como dados independentes para avaliar os modelos. O modelo não linear (WE estimou com precisão as datas de espigamento

  20. Lógica nebulosa para avaliar riscos na auditoria Fuzzy logic for risk assessment in auditing

    Directory of Open Access Journals (Sweden)

    Jerônimo Antunes

    2006-08-01

    Full Text Available A avaliação dos riscos de que os controles internos de uma entidade possam falhar constitui-se em significativo desafio para os auditores independentes de demonstrações contábeis. As metodologias de trabalho empregadas para tal finalidade, normalmente, utilizam a lógica clássica, ou também denominada binária, presumindo que os fatores de riscos estão presentes, ou não, em um determinado tipo de processo de controle. O objetivo deste trabalho foi conceber um modelo de avaliação de risco dos controles internos de uma entidade utilizando a lógica nebulosa (fuzzy logic, para contemplar os elementos difusos que compõem os fatores desse tipo de risco analisados na auditoria de demonstrações contábeis. A validação conceitual do modelo concebido foi realizada por meio de entrevistas e debates com especialistas em auditoria de demonstrações contábeis e com consultas a bibliografias relevantes pertinentes. Como conclusão do estudo, ficou patente que o modelo de avaliação de risco, com o uso da lógica nebulosa, elimina a restrição binária da lógica clássica e permite tratar, de forma quantitativa, conceitos ambíguos através da aplicação de uma escala psicométrica, para refl etir predicados tais como: "muito bom", "bom", "razoável", "de grande importância", "de pouca importância" etc., tendo potencial para produzir resultados mais amplos e próximos da realidade.The assessment of the risks that an entity's internal control system may fail represents a significant challenge to independent auditors. The methodologies used to audit financial statements are usually supported by classical logic, also called binary logic, departing from the relatively simplistic premise that risk factors are either present or not in a certain kind of control process. This study aimed to conceive a risk assessment model for an entity's internal control system, using the fuzzy logic approach, to take into account the diffuse elements that

  1. Avaliação do papel do sistema canabidiol em um modelo de lesão renal por isquemia/reperfusão em animais

    Directory of Open Access Journals (Sweden)

    Rodrigo Zon Soares

    2015-12-01

    Full Text Available RESUMO Objetivo: Investigar os efeitos da administração de canabidiol em um modelo de isquemia/reperfusão renal em animais. Métodos: Foi induzida uma lesão renal, por meio de 45 minutos de isquemia renal seguida por reperfusão. Administrou-se canabidiol (5mg/kg imediatamente após a reperfusão. Resultados: A isquemia/reperfusão aumentou os níveis de interleucina 1 e fator de necrose tumoral, o que foi atenuado pelo tratamento com canabidiol. Além disso, o canabidiol foi capaz de diminuir o dano oxidativo de lipídios e proteínas, mas não os níveis de nitrito/nitrato. A lesão renal após isquemia/reperfusão pareceu ser independente da expressão dos receptores canabidiol-1 e canabidiol-2, já que não houve aumento significante desses receptores após a reperfusão. Conclusão: O tratamento com canabidiol teve um efeito protetor contra a inflamação e o dano oxidativo em um modelo de isquemia/reperfusão renal. Esses efeitos parecem não ocorrer via ativação dos receptores canabidiol-1/canabidiol-2.

  2. Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

    Science.gov (United States)

    Dewan, Mohammad W.; Huggett, Daniel J.; Liao, T. Warren; Wahab, Muhammad A.; Okeil, Ayman M.

    2015-01-01

    Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (??), plunge force (????), and welding speed (??) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (??,??,????,??????) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

  3. Prediction of the Velocity Contours in Triangular Channel with Non-uniform Roughness Distributions by Adaptive Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Sara Bardestani

    2017-09-01

    Full Text Available Triangular channels have different applications in many water and wastewater engineering problems. For this purpose investigating hydraulic characteristics of flow in these sections has great importance. Researchers have presented different prediction methods for the velocity contours in prismatic sections. Most proposed methods are not able to consider the effect of walls roughness, the roughness distribution and secondary flows. However, due to complexity and nonlinearity of velocity contours in open channel flow, there is no simple relationship that can be fully able to exactly draw the velocity contours. In this paper an efficient approach for modeling velocity contours in triangular open channels with non-uniform roughness distributions by Adaptive Neuro-Fuzzy Inference System (ANFIS has been suggested. For training and testing model, the experimental data including 1703 data in triangular channels with geometric symmetry and non-uniform roughness distributions have been used. Comparing experimental results with predicted values by model indicates that ANFIS model is capable to be used in simulation of local velocity and determining velocity contours and the independent evaluation showed that the calculated values of discharge and depth-averaged velocity from model information are precisely in conformity with experimental values.

  4. Modelo de PCP para pequenas empresas do setor alimentício

    OpenAIRE

    Carvalho, Vianey Santos de; Pacheco, Diego Augusto de Jesus

    2015-01-01

    O principal objetivo desta pesquisa foi desenvolver e implantar um modelo de Planejamento e Controle da Produção no contexto das pequenas empresas do setor alimentício brasileiro. Os principais procedimentos metodológicos adotados foram a abordagem quantitativa e qualitativa e o estudo de caso para avaliar o desempenho do modelo. O estudo de caso contemplou o desenvolvimento de um modelo específico para o setor alimentício e a aplicação de atividades de PCP de acordo com as necessidades da pe...

  5. ESTRUTURAÇÃO DE UM MODELO MULTICRITÉRIO PARA AVALIAR O DESEMPENHO DA TUTORIA DE EDUCAÇÃO Á DISTÂNCIA

    Directory of Open Access Journals (Sweden)

    Sandra Rolim Ensslin

    2012-07-01

    Full Text Available Este trabalho concentra-se no campo da Educação à Distância, modalidade de ensino e aprendizagem em que a construção do conhecimento é intermediada pelo uso da tecnologia e que necessita de uma equipe multidisciplinar para ser ofertada. Neste contexto destaca-se a figura do tutor, profissional que realiza a mediação entre o professor e o aluno. Ao considerar a importância do tutor no âmbito da EaD, e a necessidade do coordenador da equipe em ter um processo que permita identificar, organizar, mensurar e gerenciar os critérios julgados como relevantes ao contexto, surge a seguinte questão: Quais são os critérios a serem considerados em um processo de avaliação do desempenho da tutoria de um curso de graduação na modalidade à distância? Assim, este estudo tem por objetivo estruturar um modelo multicritério para avaliar o desempenho da tutoria do curso de Ciências Contábeis à Distância, da Universidade Federal de Santa Catarina, com vistas a seu gerenciamento. O estudo de caso, de caráter exploratório utiliza como instrumento de intervenção a Metodologia Multicritério de Apoio à Decisão Construtivista (MCDA-C devido a sua capacidade de ampliar o conhecimento sobre o contexto estudado. A partir do conhecimento gerado e do modelo estruturado, identificou-se sessenta e nove critérios que respondem pelo desempenho da equipe de tutoria. Estes critérios foram mensurados ordinalmente possibilitando a visualização do perfil de desempenho da tutoria e permitindo ao gestor gerenciá-los. Os resultados obtidos foram ainda confrontados com a literatura selecionada destacando a importância e originalidade deste estudo.

  6. Impactos da adoção de um modelo para gestão da cadeia de suprimentos : um estudo de caso em uma empresa de alimentos

    OpenAIRE

    Alves, Pedro Henrique Bortolotto Fagundes

    2015-01-01

    A Gestão da Cadeia de Suprimentos, também conhecida pelo seu termo em inglês (Supply Chain Management - SCM), tem atraído cada vez mais a atenção de pesquisadores e executivos, na medida em que seus benefícios vêm sendo obtidos em empresas de diversos segmentos e portes. O presente trabalho tem o objetivo de apresentar os impactos da adoção de um modelo proposto à SCM em uma empresa do ramo de alimentos, mais especificamente em suas cadeias de lácteos e frangos. Foi verificada na literat...

  7. Developing a multipurpose sun tracking system using fuzzy control

    Energy Technology Data Exchange (ETDEWEB)

    Alata, Mohanad [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan)]. E-mail: alata@just.edu.jo; Al-Nimr, M.A. [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan); Qaroush, Yousef [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan)

    2005-05-01

    The present work demonstrates the design and simulation of time controlled step sun tracking systems that include: one axis sun tracking with the tilted aperture equal to the latitude angle, equatorial two axis sun tracking and azimuth/elevation sun tracking. The first order Sugeno fuzzy inference system is utilized for modeling and controller design. In addition, an estimation of the insolation incident on a two axis sun tracking system is determined by fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm, along with least square estimation (LSE), generates the fuzzy rules that describe the relationship between the input/output data of solar angles that change with time. The fuzzy rules are tuned by an adaptive neuro-fuzzy inference system (ANFIS). Finally, an open loop control system is designed for each of the previous types of sun tracking systems. The results are shown using simulation and virtual reality. The site of application is chosen at Amman, Jordan (32 deg. North, 36 deg. East), and the period of controlling and simulating each type of tracking system is the year 2003.

  8. O ensino de biblioteconomia no Brasil: questões acerca do modelo deweyano

    Directory of Open Access Journals (Sweden)

    Francisco das Chagas de Souza

    1997-04-01

    Full Text Available Parte da idéia de que há um modelo Deweyano de Organização da Informação e que a este liga-se um modelo de Educação bibliotecária. Ambos, enquanto prática e formação de recursos humanos, são enformados por um paradigma geral, o Paradigma da Estabilidade da Organização da Informação.

  9. Neuro-fuzzy Classification System for Wireless-Capsule Endoscopic Images

    OpenAIRE

    Vassilis S. Kodogiannis; John N. Lygouras

    2008-01-01

    In this research study, an intelligent detection system to support medical diagnosis and detection of abnormal lesions by processing endoscopic images is presented. The images used in this study have been obtained using the M2A Swallowable Imaging Capsule - a patented, video color-imaging disposable capsule. Schemes have been developed to extract texture features from the fuzzy texture spectra in the chromatic and achromatic domains for a selected region of interest from ...

  10. Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia

    Science.gov (United States)

    Karimi, Sepideh; Kisi, Ozgur; Shiri, Jalal; Makarynskyy, Oleg

    2013-03-01

    Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1 h, 24 h, 48 h and 72 h. The used ANN models were trained using three different algorithms, namely, Levenberg-Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals.

  11. Planejamento do balanço bancário: desenvolvimento de um modelo matemático de otimização do retorno econômico ajustado ao risco

    Directory of Open Access Journals (Sweden)

    Solange Garcia dos Reis

    2001-08-01

    Full Text Available Este trabalho consiste na concepção de um modelo matemático de otimização para auxiliar a escolha das categorias de ativos e passivos que comporão a estrutura patrimonial de uma instituição bancária em períodos futuros. O modelo de decisão dos gestores bancários, o qual é traduzido pelo modelo matemático, considera a necessidade de um processo de planejamento estruturado, no qual são escolhidas as alternativas que proporcionarão o maior retorno econômico e, simultaneamente, considera a necessidade de atendimento às limitações do ambiente operacional dos bancos: suas políticas internas em relação ao risco e liquidez, os relacionamentos contábeis do balanço a cada período do horizonte de planejamento, os requisitos normativos exigidos pelos reguladores bancários e as restrições relativas aos volumes de negociação suportáveis pelo mercado. A principal restrição abordada no modelo é a limitação de capital próprio para cobertura de riscos do portfólio. Esse aspecto é contemplado da perspectiva dos reguladores, por meio da modelagem dos requerimentos normativos de capital, e da perspectiva dos gestores, por meio da proposta de utilização de um modelo estatístico para mensuração do risco. O modelo matemático construído é caracterizado como um modelo de programação não-linear, multiperiódico e probabilístico.This paper is concerned with the construction of a mathematical optimization model that helps to choose the categories of assets and liabilities that will form the balance sheet of a bank in future periods. The mathematical model herein represents a decision model for bank managers. In its assumptions, it considers the necessity of a structural planning, in which there some alternatives have to be chosen to provide a better economic return. In addition, it also considers the necessity of meeting the constraints of a bank's operational environment: politics established by the Company regarding risk

  12. On-line self-learning time forward voltage prognosis for lithium-ion batteries using adaptive neuro-fuzzy inference system

    Science.gov (United States)

    Fleischer, Christian; Waag, Wladislaw; Bai, Ziou; Sauer, Dirk Uwe

    2013-12-01

    The battery management system (BMS) of a battery-electric road vehicle must ensure an optimal operation of the electrochemical storage system to guarantee for durability and reliability. In particular, the BMS must provide precise information about the battery's state-of-functionality, i.e. how much dis-/charging power can the battery accept at current state and condition while at the same time preventing it from operating outside its safe operating area. These critical limits have to be calculated in a predictive manner, which serve as a significant input factor for the supervising vehicle energy management (VEM). The VEM must provide enough power to the vehicle's drivetrain for certain tasks and especially in critical driving situations. Therefore, this paper describes a new approach which can be used for state-of-available-power estimation with respect to lowest/highest cell voltage prediction using an adaptive neuro-fuzzy inference system (ANFIS). The estimated voltage for a given time frame in the future is directly compared with the actual voltage, verifying the effectiveness and accuracy of a relative voltage prediction error of less than 1%. Moreover, the real-time operating capability of the proposed algorithm was verified on a battery test bench while running on a real-time system performing voltage prediction.

  13. APPLICATION OF FUZZY ANALYTIC HIERARCHY PROCESS TO BUILDING RESEARCH TEAMS

    Directory of Open Access Journals (Sweden)

    Karol DĄBROWSKI

    2016-01-01

    Full Text Available Building teams has a fundamental impact for execution of research and development projects. The teams appointed for the needs of given projects are based on individuals from both inside and outside of the organization. Knowledge is not only a product available on the market but also an intangible resource affecting their internal and external processes. Thus it is vitally important for businesses and scientific research facilities to effectively manage knowledge within project teams. The article presents a proposal to use Fuzzy AHP (Analytic Hierarchy Process and ANFIS (Adaptive Neuro Fuzzy Inference System methods in working groups building for R&D projects on the basis of employees skills.

  14. Application of Fuzzy Analytic Hierarchy Process to Building Research Teams

    Science.gov (United States)

    Dąbrowski, Karol; Skrzypek, Katarzyna

    2016-03-01

    Building teams has a fundamental impact for execution of research and development projects. The teams appointed for the needs of given projects are based on individuals from both inside and outside of the organization. Knowledge is not only a product available on the market but also an intangible resource affecting their internal and external processes. Thus it is vitally important for businesses and scientific research facilities to effectively manage knowledge within project teams. The article presents a proposal to use Fuzzy AHP (Analytic Hierarchy Process) and ANFIS (Adaptive Neuro Fuzzy Inference System) methods in working groups building for R&D projects on the basis of employees skills.

  15. Neuro-Fuzzy Computational Technique to Control Load Frequency in Hydro-Thermal Interconnected Power System

    Science.gov (United States)

    Prakash, S.; Sinha, S. K.

    2015-09-01

    In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.

  16. Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Metin Ertunc, H. [Department of Mechatronics Engineering, Kocaeli University, Umuttepe, 41380 Kocaeli (Turkey); Hosoz, Murat [Department of Mechanical Education, Kocaeli University, Umuttepe, 41380 Kocaeli (Turkey)

    2008-12-15

    This study deals with predicting the performance of an evaporative condenser using both artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. For this aim, an experimental evaporative condenser consisting of a copper tube condensing coil along with air and water circuit elements was developed and equipped with instruments used for temperature, pressure and flow rate measurements. After the condenser was connected to an R134a vapour-compression refrigeration circuit, it was operated at steady state conditions, while varying both dry and wet bulb temperatures of the air stream entering the condenser, air and water flow rates as well as pressure, temperature and flow rate of the entering refrigerant. Using some of the experimental data for training, ANN and ANFIS models for the evaporative condenser were developed. These models were used for predicting the condenser heat rejection rate, refrigerant temperature leaving the condenser along with dry and wet bulb temperatures of the leaving air stream. Although it was observed that both ANN and ANFIS models yielded a good statistical prediction performance in terms of correlation coefficient, mean relative error, root mean square error and absolute fraction of variance, the accuracies of ANFIS predictions were usually slightly better than those of ANN predictions. This study reveals that, having an extended prediction capability compared to ANN, the ANFIS technique can also be used for predicting the performance of evaporative condensers. (author)

  17. Neuro-Fuzzy Prediction of Cooperation Interaction Profile of Flexible Road Train Based on Hybrid Automaton Modeling

    Directory of Open Access Journals (Sweden)

    Banjanovic-Mehmedovic Lejla

    2016-01-01

    Full Text Available Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS, since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS. The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error, coefficient of determination (R2 and Pearson coefficient (r, were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.

  18. A Novel Technique for Maximum Power Point Tracking of a Photovoltaic Based on Sensing of Array Current Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

    Science.gov (United States)

    El-Zoghby, Helmy M.; Bendary, Ahmed F.

    2016-10-01

    Maximum Power Point Tracking (MPPT) is now widely used method in increasing the photovoltaic (PV) efficiency. The conventional MPPT methods have many problems concerning the accuracy, flexibility and efficiency. The MPP depends on the PV temperature and solar irradiation that randomly varied. In this paper an artificial intelligence based controller is presented through implementing of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to obtain maximum power from PV. The ANFIS inputs are the temperature and cell current, and the output is optimal voltage at maximum power. During operation the trained ANFIS senses the PV current using suitable sensor and also senses the temperature to determine the optimal operating voltage that corresponds to the current at MPP. This voltage is used to control the boost converter duty cycle. The MATLAB simulation results shows the effectiveness of the ANFIS with sensing the PV current in obtaining the MPPT from the PV.

  19. Estruturação de um modelo de custeio híbrido para uma fundação de apoio universitária

    Directory of Open Access Journals (Sweden)

    Fernando Richartz

    2011-09-01

    Full Text Available O objetivo deste estudo é estruturar um modelo de custeio híbrido (ABC e UEP para quantificação dos gastos necessários ao gerenciamento dos projetos de pesquisa de uma fundação de apoio universitária. Como objeto de estudo, utiliza-se o convênio Pequim Internacional 2, desenvolvido pela Fundação de Amparo a Pesquisa e Extensão Universitária. Os dados necessários para a estruturação do modelo foram coletados por meio de verificação in loco e junto às gerências da fundação, em especial, com a gerência financeira, durante o segundo semestre de 2010. O modelo foi estruturado em oito etapas, desenvolvido com base na literatura disponível sobre o assunto. Sua aplicação evidenciou um custo de R$ 13.330,55 para o projeto em questão. Assim, no mínimo este valor deve ser ressarcido para a fundação a título de despesas administrativas. Em relação ao total do objeto conveniado (R$ 250.000,00, de acordo com os resultados obtidos com a aplicação do modelo, os gastos da fundação encontram-se dentro do limite de quinze por cento (R$ 37.500,00 estipulados pela Portaria Interministerial nº 127 de 29 de maio de 2008. Cabe ressaltar que, além do custeamento de convênios, esta metodologia se apresenta viável como instrumento de gestão para as fundações, tendo em vista que permite o mapeamento das atividades desenvolvidas e sua mensuração e controle, o que potencializa o gerenciamento dos pontos deficitários da organização.

  20. OTIMIZAÇÃO USANDO ALGORITMO GENÉTICO DE UM MODELO DE PROPAGÇÃO BASEADO EM EQUAÇÕES PARABÓLICAS

    Directory of Open Access Journals (Sweden)

    Antonio Carlos Vilanova

    2013-01-01

    Full Text Available Este artigo apresenta uma avaliação metodológica para otimizar parâmetros em um conhecido modelo de propagação de ondas de rádio na troposfera. O modelo de propagação é baseado no Divisor de passos de Fourier para resolver equações parabólicas. Nossa abordagem utiliza algoritmo genético para determinar os valores dos parâmetros que maximize a intensidade de campo em uma determinada posição do observador. Usando algoritmo genético o tempo necessário na busca dos parâmetros ótimos é reduzido significativamente. A avaliação preliminar dos resultados através da simulação mostra que a nossa abordagem é promissora.

  1. Desenvolvimento de um modelo fractal para a estimativa da condutividade hidráulica de solos não saturados A fractal model to estimate the unsaturated hydraulic conductivity of soils

    Directory of Open Access Journals (Sweden)

    Carlos Fuentes

    2005-02-01

    Full Text Available Baseado nos conceitos da geometria fractal e nas leis de Laplace e de Poiseuille, foi criado um modelo geral para estimar a condutividade hidráulica de solos não saturados, utilizando a curva de retenção da água no solo, conforme representada por um modelo em potência. Considerando o fato de que este novo modelo da condutividade hidráulica introduz um parâmetro de interpolação ainda desconhecido, e que, por sua vez, depende das propriedades dos solos, a validação do modelo foi realizada, utilizando dois valores-limite fisicamente representativos. Para a aplicação do modelo, os parâmetros de forma da curva de retenção da água no solo foram escolhidos de maneira a se obter o modelo de van Genuchten. Com a finalidade de obter fórmulas algébricas da condutividade hidráulica, foram impostas relações entre seus parâmetros de forma. A comparação dos resultados obtidos com o modelo da condutividade e a curva experimental da condutividade dos dois solos, Latossolo Vermelho-Amarelo e Argissolo Amarelo, permitiu concluir que o modelo proposto é simples em sua utilização e é capaz de predizer satisfatoriamente a condutividade hidráulica dos solos não saturados.From a conceptual model based on fractal geometry and Laplace's and Poiseuille's laws, a versatile and general fractal model for the hydraulic conductivity to be used in the soils was developed. The soil-moisture retention curve is derived from a power model. Due to the fact that the proposed model of hydraulic conductivity introduces a still unknown interpolation parameter, which in turn is a function of soil properties, its limiting values were considered for the analysis. To apply the model in the soil, the form parameters of the soil-moisture retention curve were chosen so as to reproduce van Genuchten's equation. In order to obtain a closed-form equation for the hydraulic conductivity, relationships between the form parameters were imposed. The comparison between

  2. Use of an adaptive neuro-fuzzy inference system to obtain the correspondence among balance, gait, and depression for Parkinson's disease

    Science.gov (United States)

    Woo, Youngkeun; Lee, Juwon; Hwang, Sujin; Hong, Cheol Pyo

    2013-03-01

    The purpose of this study was to investigate the associations between gait performance, postural stability, and depression in patients with Parkinson's disease (PD) by using an adaptive neuro-fuzzy inference system (ANFIS). Twenty-two idiopathic PD patients were assessed during outpatient physical therapy by using three clinical tests: the Berg balance scale (BBS), Dynamic gait index (DGI), and Geriatric depression scale (GDS). Scores were determined from clinical observation and patient interviews, and associations among gait performance, postural stability, and depression in this PD population were evaluated. The DGI showed significant positive correlation with the BBS scores, and negative correlation with the GDS score. We assessed the relationship between the BBS score and the DGI results by using a multiple regression analysis. In this case, the GDS score was not significantly associated with the DGI, but the BBS and DGI results were. Strikingly, the ANFIS-estimated value of the DGI, based on the BBS and the GDS scores, significantly correlated with the walking ability determined by using the DGI in patients with Parkinson's disease. These findings suggest that the ANFIS techniques effectively reflect and explain the multidirectional phenomena or conditions of gait performance in patients with PD.

  3. O desenvolvimento de um modelo de assistência continua ao parto Desarrollo de un modelo de asistencia continua en el parto Development of a delivery continued care model

    Directory of Open Access Journals (Sweden)

    Ana Cláudia Silveira Lambert

    2010-07-01

    Full Text Available Objetivo. Descrever as experiências das enfermeiras obstétricas relacionadas com o desenvolvimento de um modelo de assistência ao parto. Metodologia. investigação do tipo estudo de caso. Analisaramse as declarações de quatro enfermeiras obstétricas, que desenvolveram o modelo de assistência ao parto proposto pelo Hospital Universitário da Universidade de São Paulo. A informação obtida se analisou de modo indutivo e interpretativo. Resultados. das narrativas surgiram quatro categorias descritivas: a a convivência com o sentimento de ambivalência, b o trabalho com facilidades e dificuldades com a adoção do novo modelo, c as mudanças no conceito da assistência ao parto, e d os benefícios proporcionados pelo modelo. As enfermeiras reconhecem a viabilidade do modelo, mas sua implementação definitiva na instituição depende das mudanças nas diretrizes filosóficas e gerenciais relacionadas com a assistência ao parto e do compromisso dos profissionais. Conclusão. Reconheceramse os diversos aspectos envolvidos na implementação do modelo de atendimento no parto.Objetivo. Describir las experiencias de las enfermeras obstétricas relacionadas con el desarrollo de un modelo de asistencia al parto. Metodología. Investigación del tipo estudio de caso. Se analizaron las declaraciones de cuatro enfermeras obstétricas, que desarrollaron el modelo de asistencia al parto propuesto por el Hospital Universitario de la Universidad de São Paulo. La información obtenida se analizó de modo inductivo e interpretativo. Resultados. De las narrativas surgieron cuatro categorías descriptivas: a la convivencia con el sentimiento de ambivalencia, b el trabajo con facilidades y dificultades con la adopción del nuevo modelo, c los cambios en el concepto de la asistencia al parto, y d los beneficios proporcionados por el modelo. Las enfermeras reconocen la viabilidad del modelo, pero su implementación definitiva en la institución depende de

  4. Um modelo de condução do processo de outsourcing e um estudo de caso na indústria de processamento químico A model for guiding outsourcing process and a case study in the chemical processing industry

    Directory of Open Access Journals (Sweden)

    Hercules B. Vernalha

    2005-08-01

    Full Text Available Estudos tratando do processo de outsourcing em importantes setores industriais não-líderes ao redor do mundo, bem como propondo modelos práticos para a condução do processo de repasse de atividades de manufatura como um todo, e não somente no estágio de decisão, são difíceis de encontrar. Nesse contexto, este artigo procura apresentar um modelo prático para conduzir o processo de outsourcing na sua totalidade, dividindo-o em quatro estágios principais (motivação, decisão, implementação e gestão. O modelo foi aplicado com sucesso em um caso envolvendo duas companhias multinacionais operando no Brasil, em uma cadeia de suprimentos do setor de resinas fenólicas. Os resultados ressaltam, principalmente, a adequação do modelo proposto, no sentido de enfatizar os desafios em cada fase e as habilidades necessárias para enfrentá-los, bem como a importância da construção de parcerias baseadas na confiança e na integração por meio da tecnologia da informação.Studies related to the outsourcing process in several important non-leading industries worldwide and also regarding the existence of practical models and frameworks to guide the entire process and not only the decision-making stage are rare to find. In this context, this article presents a practical model to guide the whole outsourcing process, divided into four main stages (motivation, decision, implementation and management. It was successfully applied in a case involving two multinational companies operating in Brazil, in a supply chain of the phenolic resin industry. The results highlighted mainly the adequacy of the proposed model, as well as the importance of constructing a partnership based on trust and integration by information technology.

  5. Vivenciando a experiência da parturição em um modelo assistencial humanizado Viviendo la experiencia de la parturición en un modelo asistencial humanizado Living the birth process in a humanized assistance model

    Directory of Open Access Journals (Sweden)

    Larissa Mandarano da Silva

    2011-02-01

    Full Text Available Tratou-se de um estudo qualitativo baseado na abordagem fenomenológica com o objetivo de compreender as experiências de puérperas que vivenciaram o trabalho de parto e o parto em um modelo assistencial humanizado. Os dados foram coletados em um hospital localizado na cidade de São Paulo, onde foram entrevistadas oito puérperas. Da análise dos dados surgiram os temas: Suportando o trabalho de parto e Tendo a oportunidade de resgatar a autonomia, e o fenômeno desvelado foi "Vivendo a ambiguidade da parturição em um modelo assistencial humanizado". Os relatos evidenciaram sentimentos como dor, medo e ansiedade, porém, possibilitou a participação e resgate da autonomia. Embora o estudo tenha sido realizado em um modelo assistencial considerado humanizado, as experiências das puérperas revelam que ainda se distanciam de uma efetiva humanização, conforme seus princípios. Este estudo pode ser utilizado para nortear ações educativas voltadas à humanização e gerar mudanças assistenciaisEste es un estudio cualitativo con abordaje fenomenológica con la finalidad de comprender las experiencias de puerperas que vivieron el trabajo de parto y el parto en la asistencia humanizada. La colecta de los dados ocurrio en un hospital en la ciudad de São Paulo, donde fuera entrevistadas ocho mujeres en el periodo post-partum. De la analisis de los dados surgieron los temas: Soportando el trabajo de parto y Tiendo la oportunidad de rescatar la autonomía, siendo desvelado lo fenómeno "Viviendo la ambigüedad de la parturición en un modelo asistencial humanizado". Los relatos evidenciaron sentimientos relacionados a dolor, miedo y ansiedad, pero, posibilitou la participación y rescate de la autonomía. Aunque el estudio tener sido realizad en la asistencia humanizada, las experiencias de las puérperas fueram distantes de los principios de la asistencia humanizada. El actual estudio puede nortear aciones educativas al parto direccionadas para

  6. Real-time process signal validation based on neuro-fuzzy and possibilistic approach

    International Nuclear Information System (INIS)

    Figedy, S.; Fantoni, P.F.; Hoffmann, M.

    2001-01-01

    Real-time process signal validation is an application field where the use of fuzzy logic and Artificial Neural Networks can improve the diagnostics of faulty sensors and the identification of outliers in a robust and reliable way. This study implements a fuzzy and possibilistic clustering algorithm to classify the operating region where the validation process is to be performed. The possibilistic approach allows a fast detection of unforeseen plant conditions. Specialized Artificial Neural Networks are used, one for each fuzzy cluster. This offers two main advantages: the accuracy and generalization capability is increased compared to the case of a single network working in the entire operating region, and the ability to identify abnormal conditions, where the system is not capable to operate with a satisfactory accuracy, is improved. This system analyzes the signals, which are e.g. the readings of process monitoring sensors, computes their expected values and alerts if real values are deviated from the expected ones more than limits allow. The reliability level of the current analysis is also produced. This model has been tested on a simulated data from the PWR type of a nuclear power plant, to monitor safety-related reactor variables over the entire power-flow operating map and were installed in real conditions of BWR nuclear reactor. (Authors)

  7. Prediction of Scour Depth around Bridge Piers using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

    Science.gov (United States)

    Valyrakis, Manousos; Zhang, Hanqing

    2014-05-01

    Earth's surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health of river systems but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the application of a Machine Learning model that has been successfully employed in Water Engineering, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation.

  8. Deflação, desemprego e recuperação econômica: um modelo keynesiano

    Directory of Open Access Journals (Sweden)

    João Sicsú

    2000-12-01

    Full Text Available Este artigo mostra que a hipótese de salários e preços perfeitamente flexíveis é insuficiente para garantir que o efeito oriundo de uma queda na demanda autônoma seja eliminado, ou seja, a restauração do equilíbrio original não se verifica. Um modelo keynesiano é elaborado para demonstrar que, sob tais condições, uma redução na demanda levaria a uma nova redução da demanda e, portanto, ao aprofundamento da recessão inicial. O resultado obtido sugere que o equilíbrio original pode ser restabelecido, por exemplo, por meio de um aumento dos gastos governamentais capaz de reverter a expectativa da economia, desencadeando um processo de recuperação econômica.This paper reveals that the hypothesis of fully flexible prices and wages is insufficient to guarantee that the effect of a decrease in the autonomous demand could be eliminated. In other words, the return to the original equilibrium would never take place. A Keynesian model is elaborated to demonstrate that a decrease in demand induces another decrease in demand and thus increases recession. The findings indicate that the original equilibrium could be restored, for instance, by way of an increase of government expenditures which would be able to change the expectational state of economy, starting a process of recovery.

  9. Proposta de um modelo de gestão da qualidade para micro e pequenas empresas integrando a estratégia seis sigma à NBR ISO 9001

    OpenAIRE

    Costa, Filipe Cunha Reges da

    2009-01-01

    Este trabalho tem como principal objetivo propor um modelo de Gestão da Qualidade para micro e pequenas empresas integrando os modelos de gestão: estratégia Seis Sigma e NBR ISO 9001:2000. É desenvolvida uma pesquisa exploratória para levantamento das informações técnicas e bibliográficas existentes sobre as duas propostas, com ênfase em sua integração. Em seguida, procede-se a uma pesquisa de opinião, por meio de questionário, realizada com 65 analistas/consultores de Sistemas de Gestão d...

  10. Developing a local least-squares support vector machines-based neuro-fuzzy model for nonlinear and chaotic time series prediction.

    Science.gov (United States)

    Miranian, A; Abdollahzade, M

    2013-02-01

    Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.

  11. Comparação Entre Os Modelos Residual Income Valuation (RIV, Abnormal Earnings Growth (AEG e Fluxo de Caixa Livre (FCF: Um estudo empírico no mercado de capitais brasileiro

    Directory of Open Access Journals (Sweden)

    Eric Serrano Ferreira

    2008-01-01

    Full Text Available O objetivo deste estudo foi comparar os modelos Residual Income Valuation (RIV, Abnormal Earnings Growth (AEG e Fluxo de Caixa Livre (FCF no mercado brasileiro. Fez-se um teste empírico para comparar os três modelos, utilizando dados das empresas listadas na BOVESPA e testando a afirmação para o mercado de capitais brasileiro. Cada modelo foi analisado por meio da técnica estatística de regressão múltipla, anualmente, observando assim o comportamento dos modelos ao longo dos anos de 1995 a 2002. Ao serem realizados os testes, pode-se concluir que, de 1995 à 1999, o modelo RIV possuía poder explicativo superior aos outros dois modelos e a partir de 2000, os modelos AEG e RIV se equivalem, ilustrando o desenvolvimento do mercado brasileiro nos últimos anos. O modelo FCF apresentou o menor poder explicativo em todos os anos analisados. Os resultados encontrados se confirmaram por meio da análise de dados em painel.

  12. A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller

    Science.gov (United States)

    Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi

    This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.

  13. Avaliação de empresas: um estudo comparativo entre o modelo de capitalização dos lucros e o modelo dos múltiplos do fluxo de caixa

    OpenAIRE

    Sady Mazzioni; Omeri Dedonatto; José Luis de Castro Neto; Fábio Matiello Neis

    2005-01-01

    O debate acerca dos processos de avaliação de empresas está em plena efervescência, instigado pela globalização dos mercados e pelo dinamismo do mundo dos negócios. Os fatores que contribuem de modo mais consistente para isso compreendem desde o interesse nos procedimentos de fusão, incorporação, cisão e alienação até a necessidade da informação para algum objetivo qualquer. O objetivo principal deste estudo é apresentar um enfoque no modelo de capitalização dos lucros comparativamente ao mod...

  14. Proposição de um modelo baseado em Customer Lifetime Value para a análise de melhorias no sistema produtivo Proposition of a Customer Lifetime Value model to evaluate production system improvements

    Directory of Open Access Journals (Sweden)

    Luís Felipe Riehs Camargo

    2011-01-01

    Full Text Available Este artigo propõe um modelo para analisar melhorias no sistema produtivo à luz do valor do cliente para a empresa. Para isso, apresenta um modelo que agrega variáveis advindas do processo produtivo, do mercado consumidor e de métricas do marketing. Os três pilares de sustentação do modelo são os clientes, com suas necessidades e percepções em relação aos produtos e serviços; as características atribuídas pela produção; e as regras para análise do impacto da adoção de diferentes melhorias no sistema produtivo sobre o valor vitalício dos clientes atuais (CLV. Além disso, um modelo de escolha discreta (logit é proposto como o integrador entre a produção e o marketing. O método de pesquisa utilizado foi composto de quatro etapas: levantamento de referencial; construção do modelo conceitual; aplicação em um caso; análise e discussão. Como resultados, observa-se que, havendo disponibilidade de informações e tempo para sua condução, o modelo contribui para a identificação de melhorias que contemplem simultaneamente a visão de produção e a de sustentabilidade financeira.This paper aims to propose a model to investigate the effect of improvements on the productive system on the organization considering customer value. The model proposed combine variables representing the productive process, market, and marketing metrics. It consists of three fundamental elements: consumers and their needs and perceptions of products and services; characteristics of the productive system; and rules to analyze the impact of adoption of different improvements on the productive system. Customer lifetime value (CLV was measured and a discrete choice model (logit is proposed as an integrator between production and marketing perspectives. The research methodology consisted of four steps: referential research, conceptual modeling, application in a particular case, and results analysis. As a result, it is observed that upon availability

  15. MODELO BORROSO PARA EVALUAR LOS FACTORES QUE AFECTAN LA PRODUCTIVIDAD EN LA PYMES // FUZZY MODEL TO ASSESS FACTORS AFFECTING PRODUCTIVITY IN PYME

    Directory of Open Access Journals (Sweden)

    Roxana Martínez Sánchez

    2010-06-01

    Full Text Available En Venezuela, las pequeñas y medianas empresas (PYME contribuyen considerablemente a la actividad económica y son un gran generador de empleo. Este trabajo plantea el desarrollo de un modelo borroso que permita evaluar los factores de producción que afectan la productividad, con el fin de estimar su impacto y minimizarlos para que de esta manera la empresa no pierda su nivel del aprovechamiento de los recursos. La importancia del mismo radica en la utilización de la lógica borrosa para la evaluación del efecto de factores como costo de producción, producto y satisfacción del cliente en la productividad de las PYME.// Abstract: In Venezuela, small- and medium sized companies PYME contribute considerably to economic activity and are a great employment generator. This article state a fuzzy model development which permit to evaluate the production factors that affect productivity, in order to estimate and minimize their impact and, in this way the company dosen‘t loose the use or resource level. The importance of this development lies in the use of fuzzy logic to evaluate the effect of factors like production and client satisfaction in PYME‘s productivity.

  16. Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder.

    Directory of Open Access Journals (Sweden)

    Shahaboddin Shamshirband

    Full Text Available Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean, Intruder Rear sensors active (boolean, Agent Front sensors active (boolean, Agent Rear sensors active (boolean, RSSI signal intensity/strength (integer, Elapsed time (in seconds, Distance between Agent and Intruder (m, Angle of Agent relative to Intruder (angle between vehicles °, Altitude difference between Agent and Intruder (m influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles ° is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

  17. Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder.

    Science.gov (United States)

    Shamshirband, Shahaboddin; Banjanovic-Mehmedovic, Lejla; Bosankic, Ivan; Kasapovic, Suad; Abdul Wahab, Ainuddin Wahid Bin

    2016-01-01

    Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

  18. Modelo gerencial de mensuração do portfólio de patentes de um centro de pesquisa e desenvolvimento em telecomunicações

    OpenAIRE

    Adriano, Eunice

    2014-01-01

    Este trabalho propõe um modelo de mensuração gerencial do portfólio de patentes de um centro de pesquisa e desenvolvimento em telecomunicações. Para tanto, apresenta-se o conceito de propriedade intelectual, pois considera-se que a compreensão do conhecimento como ferramenta estratégica para as organizações e o seu uso do conhecimento na sociedade demonstra a importância do setor de informação para a economia de todos os países. Ao se expor a questão de Patentes o seu valor indica que a criaç...

  19. MEECA: um modelo econométrico espacial para projeção consistente de culturas agropecuárias

    Directory of Open Access Journals (Sweden)

    Eduardo Simões de Almeida

    2004-09-01

    Full Text Available O objetivo do artigo é apresentar o modelo MEECA, que representa uma metodologia econométrica inovadora para geração de cenários economicamente consistentes de longo prazo de projeções de culturas agropecuárias. Essa metodologia é baseada na econometria espacial, levando em conta os efeitos espaciais, a saber, a dependência e a heterogeneidade espaciais. Nas regressões incorporamos uma série de termos espaciais, tais como efeitos de transbordamento, expansão espacial dos coeficientes, defasagem espacial do erro etc. Essa metodologia é aplicada para a estimação dos modelos econométrico-espaciais de algumas culturas agropecuárias na Amazônia, a saber, a rizicultura, a sojicultura, a cultura do milho, a cultura dos outros produtos extrativos, bovinocultura e outros produtos pecuários. O modelo MEECA está diretamente integrado a um modelo de equilíbrio geral aplicado, garantindo a consistência sistêmica das projeções.The objective of this article is to present the MEECA model, an innovative econometric methodology for generating economically consistent long term scenarios of projections for agriculture and living stock. This methodology is based upon the spatial econometrics, taking into account the spatial effects, namely, the spatial dependence and the spatial heterogeneity. In the regressions, we incorporate various spatial terms, such as spillover effects, spatial expansion of coefficients, spatial error lag, etc. This methodology is applied for the estimation of the spatial econometric models of some agricultural and living stock products in the Amazon region, namely, rice, soy bean, corn, other extractive products (excluding wood, cattle and other living stock products. The MEECA model is directly linked to an applied general equilibrium model, guaranteeing the systemic consistency of the projections.

  20. Evaluación de la vulnerabilidad a la degradación por erosión en suelos mediante un modelo de lógica borrosa Assessment of soil vulnerability to erosion by a fuzzy logic model

    Directory of Open Access Journals (Sweden)

    I. Melendez-Pastor

    2010-01-01

    Full Text Available Se presenta un modelo simplificado para evaluar la vulnerabilidad de suelos a proce­sos degradativos, tales como la erosión. Es­tá basado en el empleo de la lógica borrosa para operar con variables predictivas con­tinuas estimadas a partir de imágenes de te­ledetección y modelos digitales de eleva­ciones. Combina variables explicativas re­lativas al estado de la cubierta vegetal y di-versos parámetros topográficos. Las esti­maciones se realizan mediante un sistema de decisión implementado en un sistema de información geográfica (SIG, obteniéndo­se mapas de vulnerabilidad que pueden ser actualizados con nuevas imágenes de saté­lite. La zona estudiada se localiza en el su­reste peninsular, caracterizado por un clima Mediterráneo semiárido. Se estimaron altas tasas de vulnerabilidad para todas las esti­maciones realizadas, en relación a los um­brales máximos y mínimos esperables. Se estudio la respuesta de los cambios de vul­nerabilidad en relación a los cambios de clases de erosión actual a potencial según el modelo USLE. Se estimó una alta corre­lación entre los cambios de clases de ero sión y los cambios de estimaciones de vul­nerabilidad.A simplified fuzzy logic model to assess soil vulnerability to degradation processes, such as soil erosion, is presented. The model is based on the use of fuzzy logic to operate with explanatory continuous variables esti­mated from remote sensing images and digi­tal elevation models. It combines explana­tory variables concerning the state of vege­tation cover and topographic parameters. The estimations are made by a decision sys­tem implemented in a geographic informa­tion system (GIS. Output vulnerability maps can be updated with new satellite im­ages. The study area is located on the south­east Iberian Peninsula, characterized by a semiarid Mediterranean climate. It was found high vulnerability for all estimations in relation to the expected maximum and minimum

  1. CULTURA ORGANIZACIONAL E GESTÃO EM EMPRESAS DE ECONOMIA CRIATIVA: UM ESTUDO COMPARATIVO COM MODELOS DE GESTÃO CONTEMPORÂNEOS

    OpenAIRE

    Ernani Cesar de Freitas; Mauro Cesar Tonidandel; Cristiano Max Pereira Pinheiro; Mauricio Barth

    2014-01-01

    Este artigo apresenta uma pesquisa sobre a cultura organizacional e a gestão em empresas de economia criativa. O tema é emergente e teve como motivação um estudo divulgado pela Federação das Indústrias do Rio de Janeiro – Sistema Firjan – divulgado em 2008. O presente trabalho visa mostrar características da cultura organizacional que diferenciam as empresas de economia criativa de seus concorrentes, no que se refere aos modelos de gestão. Para tanto, busca-se compreender os elementos básicos...

  2. Un modelo explicativo de resiliencia en jovenes y adolescentes Um modelo explicativo de resiliência em jovens e adolescentes An explanatory model of resilience in youth and adolescents

    Directory of Open Access Journals (Sweden)

    Alicia Omar

    2011-06-01

    Full Text Available La resiliencia ha sido definida como la capacidad para superar y salir fortalecido de las adversidades. Esta capacidad es evolutiva y es influenciada por factores protectivos, tanto personales como ambientales. Si bien muchos de estos factores han sido identificados, poco se sabe acerca de las complejas interacciones entre ellos y la forma que contribuyen al desarrollo de la resiliencia. Con base en la evidencia científica más reciente, el objetivo del presente trabajo fue desarrollar un modelo explicativo de la resiliencia en jóvenes y adolescentes. Cada variable introducida en el modelo asienta sus bases en la evidencia teórica o empírica publicada. Por la naturaleza de las variables incluidas (optimismo, sentido del humor, emociones positivas, bienestar, inteligencia emocional, el modelo propuesto se enmarca dentro de los lineamientos de la Psicología Positiva. Se ofrecen sugerencias teóricas, metodológicas y epistemológicas para poner a prueba el modelo..Tem-se definido resiliência como a capacidade de superar as adversidades e sair delas mais fortalecido. Esta capacidade é evolutiva e é influenciada por fatores de proteção tanto pessoal como ambiental. Embora muitos desses fatores tenham sido identificados, pouco se sabe sobre as complexas interações entre eles e a maneira como contribuem para o desenvolvimento da resiliência. O bjetivo destes trabalho foi desenvolver, com base nos últimos dados científicos, um modelo explicativo da resiliência em jovens e adolescentes. Cada variável introduzida no modelo está enraizada na prova teórica ou empírica publicada. Pela natureza das variáveis incluídas (otimismo, senso de humor, emoções positivas, bem-estar e inteligência emocional, o modelo proposto enquadra-se nas diretrizes da Psicologia Positiva. Sugestões teóricas, metodológicas e epistemológicas são oferecidas para testar o modelo.Resilience has been defined as the ability to overcome and emerge stronger

  3. Tüketici Fiyat Endeksinin Uyarlamalı Ağa Dayalı Bulanık Çıkarım Sistemi ile Kestirimi / Consumer Price Index Forecast with Adaptive Neuro Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Serenay VAROL

    2016-04-01

    Full Text Available Son yıllarda zaman serisi tahmini için birçok alternatif yöntem önerilmiştir. Uyarlamalı ağa dayalı bulanık çıkarım sistemi (ANFIS öngörü problemi için literatürde en çok uygulanan bulanık çıkarım sistemidir. Bu çalışmada tüketici fiyat endeksinin kestiriminde ANFIS’in performansı incelenmiştir. Çalışmanın sonucunda ANFIS yöntemi ile ilgilenilen zaman aralığındaki tüketici fiyat endeksinin kestiriminde ulaşılan sonuçlar yorumlanmıştır. / Alternative methods have been proposed for time series prediction in last years. Adaptive neuro fuzzy inference system (ANFIS is the most used fuzzy inference system in literature for prediction problem. In this study, the performance of ANFIS in forecasting consumer price index is examined, and the results of the consumer price index estimation in time period, on which ANFIS method is applied, are interpreted.

  4. Estruturação de um modelo de custeio híbrido para uma fundação de apoio universitária

    Directory of Open Access Journals (Sweden)

    Fernando Richartz

    2011-06-01

    Full Text Available http://dx.doi.org/10.5007/1983-4535.2011v4n3p21   O objetivo deste estudo é estruturar um modelo de custeio híbrido (ABC e UEP para quantificação dos gastos necessários ao gerenciamento dos projetos de pesquisa de uma fundação de apoio universitária. Como objeto de estudo, utiliza-se o convênio Pequim Internacional 2, desenvolvido pela Fundação de Amparo a Pesquisa e Extensão Universitária. Os dados necessários para a estruturação do modelo foram coletados por meio de verificação in loco e junto às gerências da fundação, em especial, com a gerência financeira, durante o segundo semestre de 2010.  O modelo foi estruturado em oito etapas, desenvolvido com base na literatura disponível sobre o assunto. Sua aplicação evidenciou um custo de R$ 13.330,55 para o projeto em questão. Assim, no mínimo este valor deve ser ressarcido para a fundação a título de despesas administrativas. Em relação ao total do objeto conveniado (R$ 250.000,00, de acordo com os resultados obtidos com a aplicação do modelo, os gastos da fundação encontram-se dentro do limite de quinze por cento (R$ 37.500,00 estipulados pela Portaria Interministerial nº 127 de 29 de maio de 2008. Cabe ressaltar que, além do custeamento de convênios, esta metodologia se apresenta viável como instrumento de gestão para as fundações, tendo em vista que permite o mapeamento das atividades desenvolvidas e sua mensuração e controle, o que potencializa o gerenciamento dos pontos deficitários da organização.

  5. A prediction model of ammonia emission from a fattening pig room based on the indoor concentration using adaptive neuro fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Xie, Qiuju, E-mail: xqj197610@163.com [Institute of Information Technology, Heilongjiang Bayi Agricultural University, Daqing 163319 (China); Ni, Ji-qin [Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907 (United States); Su, Zhongbin [Institute of Electric and Information, Northeast Agricultural University, Harbin 150030 (China)

    2017-03-05

    Highlights: • A prediction model of ammonia emission was built based on the indoor ammonia concentration prediction model using ANFIS. • Five kinds of membership functions were compared to get a well fitted prediction model. • Compared with the BP and MLRM model, the ANFIS prediction model with “gbell” membership function has the best performances. - Abstract: Ammonia (NH{sub 3}) is considered one of the significant pollutions contributor to indoor air quality and odor gas emission from swine house because of the negative impact on the health of pigs, the workers and local environment. Prediction models could provide a reasonable way for pig industries and environment regulatory to determine environment control strategies and give an effective method to evaluate the air quality. The adaptive neuro fuzzy inference system (ANFIS) simulates human’s vague thinking manner to solve the ambiguity and nonlinear problems which are difficult to be processed by conventional mathematics. Five kinds of membership functions were used to build a well fitted ANFIS prediction model. It was shown that the prediction model with “Gbell” membership function had the best capabilities among those five kinds of membership functions, and it had the best performances compared with backpropagation (BP) neuro network model and multiple linear regression model (MLRM) both in wintertime and summertime, the smallest value of mean square error (MSE), mean absolute percentage error (MAPE) and standard deviation (SD) are 0.002 and 0.0047, 31.1599 and 23.6816, 0.0564 and 0.0802, respectively, and the largest coefficients of determination (R{sup 2}) are 0.6351 and 0.6483, repectively. The ANFIS prediction model could be served as a beneficial strategy for the environment control system that has input parameters with highly fluctuating, complexity, and non-linear relationship.

  6. A prediction model of ammonia emission from a fattening pig room based on the indoor concentration using adaptive neuro fuzzy inference system

    International Nuclear Information System (INIS)

    Xie, Qiuju; Ni, Ji-qin; Su, Zhongbin

    2017-01-01

    Highlights: • A prediction model of ammonia emission was built based on the indoor ammonia concentration prediction model using ANFIS. • Five kinds of membership functions were compared to get a well fitted prediction model. • Compared with the BP and MLRM model, the ANFIS prediction model with “gbell” membership function has the best performances. - Abstract: Ammonia (NH_3) is considered one of the significant pollutions contributor to indoor air quality and odor gas emission from swine house because of the negative impact on the health of pigs, the workers and local environment. Prediction models could provide a reasonable way for pig industries and environment regulatory to determine environment control strategies and give an effective method to evaluate the air quality. The adaptive neuro fuzzy inference system (ANFIS) simulates human’s vague thinking manner to solve the ambiguity and nonlinear problems which are difficult to be processed by conventional mathematics. Five kinds of membership functions were used to build a well fitted ANFIS prediction model. It was shown that the prediction model with “Gbell” membership function had the best capabilities among those five kinds of membership functions, and it had the best performances compared with backpropagation (BP) neuro network model and multiple linear regression model (MLRM) both in wintertime and summertime, the smallest value of mean square error (MSE), mean absolute percentage error (MAPE) and standard deviation (SD) are 0.002 and 0.0047, 31.1599 and 23.6816, 0.0564 and 0.0802, respectively, and the largest coefficients of determination (R"2) are 0.6351 and 0.6483, repectively. The ANFIS prediction model could be served as a beneficial strategy for the environment control system that has input parameters with highly fluctuating, complexity, and non-linear relationship.

  7. Sistema de classificação fuzzy para o risco de infestação por plantas daninhas considerando a sua variabilidade espacial Fuzzy classification system for risk of weed infestation considering spatial variability

    Directory of Open Access Journals (Sweden)

    G.M. Bressan

    2006-06-01

    Full Text Available Este artigo trata do problema de classificação do risco de infestação por plantas daninhas usando técnicas geoestatísticas, análise de imagens e modelos de classificação fuzzy. Os principais atributos utilizados para descrever a infestação incluem a densidade de sementes, bem como a sua extensão, a cobertura foliar e a agressividade das plantas daninhas em cada região. A densidade de sementes reflete a produção de sementes por unidade de área, e a sua extensão, a influência das sementes vizinhas; a cobertura foliar indica a extensão dos agrupamentos das plantas daninhas emergentes; e a agressividade descreve a porcentagem de ocupação de espécies com alta capacidade de produção de sementes. Os dados da densidade de sementes, da cobertura foliar e da agressividade para as diferentes regiões são obtidos a partir de simulação com modelos matemáticos de populações. Neste artigo propõe-se um sistema de classificação fuzzy utilizando os atributos descritos para inferir os riscos de infestação de regiões da cultura por plantas daninhas. Resultados de simulação são apresentados para ilustrar o uso desse sistema na aplicação localizada de herbicida.This paper deals with the problem of classifying the risk of infestation by weeds in a field using geoestatistics techniques, image analysis and fuzzy classification models. The main attributes used to describe the infestation include seed density, seed density patch, weed cover and aggressivity to produce seeds in each region. Seed density reflects seed production per area unit; seed density patch reflects the influence of the neighbouring seeds in a clustering; weed cover indicates the extension of the emergent weed plant clusterings; and, finally, aggressivity describes the percentage of occupation of species with high weed seed production capacity. Data for seed density, weed cover and aggressivity for the different regions are obtained from mathematical models. In

  8. Competências produtivas e visão estratégica: um modelo de gestão interativa Production competence and strategic vision: an interactive management model

    Directory of Open Access Journals (Sweden)

    Edson Pacheco Paladini

    2010-12-01

    Full Text Available Partindo da distinção conceitual entre decisão estratégica e ação estratégica, apresenta-se, aqui, um modelo que, fazendo uso da noção de competências produtivas, gera uma estrutura interativa de agregação dos elementos que compõem o diferencial competitivo de uma organização. O objetivo dessa estrutura é definir os meios de atuação da empresa no mercado e, mais em geral, na sociedade como um todo. Para fundamentar o desenvolvimento do modelo proposto foi elaborado um suporte teórico de conceitos, posições e argumentações relativas ao processo de gestão estratégica, nas suas variadas abordagens e distintos enfoques. O modelo está em processo de efetiva implantação em uma organização industrial, tendo nela sido utilizadas suas fases básicas na definição do comportamento estratégico da empresa em estudo nos últimos dois anos. A análise conceitual e as lições práticas advindas do processo de implantação efetiva do modelo permitem formular algumas conclusões (gerais e específicas acerca da proposta aqui descrita.Considering the conceptual distinction between strategic decisions and strategic actions and using production competence definitions, a general model is presented here. This model creates an interactive structure to aggregate the elements that compose the competitive differential of the organization. The objective of the structure is to define the way that organizations can act in the market, and, in general, in the whole society. To develop the model, a theoretical support structure has been defined in terms of concepts, positions and arguments related to Strategic Management, according to its different approaches. The model is now being used by an industrial organization and its basic phases have been used in the definition of the strategic behavior of the organization in question over the last two years. The conceptual analysis and the practical observations that have come from the effective

  9. Optimal inverse magnetorheological damper modeling using shuffled frog-leaping algorithm–based adaptive neuro-fuzzy inference system approach

    Directory of Open Access Journals (Sweden)

    Xiufang Lin

    2016-08-01

    Full Text Available Magnetorheological dampers have become prominent semi-active control devices for vibration mitigation of structures which are subjected to severe loads. However, the damping force cannot be controlled directly due to the inherent nonlinear characteristics of the magnetorheological dampers. Therefore, for fully exploiting the capabilities of the magnetorheological dampers, one of the challenging aspects is to develop an accurate inverse model which can appropriately predict the input voltage to control the damping force. In this article, a hybrid modeling strategy combining shuffled frog-leaping algorithm and adaptive-network-based fuzzy inference system is proposed to model the inverse dynamic characteristics of the magnetorheological dampers for improving the modeling accuracy. The shuffled frog-leaping algorithm is employed to optimize the premise parameters of the adaptive-network-based fuzzy inference system while the consequent parameters are tuned by a least square estimation method, here known as shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach. To evaluate the effectiveness of the proposed approach, the inverse modeling results based on the shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach are compared with those based on the adaptive-network-based fuzzy inference system and genetic algorithm–based adaptive-network-based fuzzy inference system approaches. Analysis of variance test is carried out to statistically compare the performance of the proposed methods and the results demonstrate that the shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system strategy outperforms the other two methods in terms of modeling (training accuracy and checking accuracy.

  10. Aplicação de um modelo matemático na simulação da produção e importação de gás natural no Brasil até 2017

    Directory of Open Access Journals (Sweden)

    Antonio Carlos Gracias

    2010-08-01

    Full Text Available O governo federal, por meio de política de desenvolvimento, pretende ampliar a participação do gás natural na matriz energética nos próximos anos. Essa ampliação irá produzir um aumento da capacidade de abastecimento de energia elétrica com uso da geração de energia térmica com gás natural. Um modelo matemático capaz de possibilitar uma previsão futura do consumo e importação de gás natural é fundamental para o planejamento da matriz energética do Brasil. Este trabalho compara dois modelos matemáticos, o modelo de Verhulst (modelo logístico e o modelo de Malthus (modelo exponencial, com o objetivo de analisar as possibilidades desses modelos descreverem a evolução da produção e importação de gás natural no Brasil até 2017, a partir dos dados fornecidos pelo Balanço Energético Nacional desde 1970 até 2007. Toda parte computacional, gráficos, resolução das equações diferenciais e cálculos de linearização e método dos mínimos quadrados, foi feita com o uso do software MatLabÒ. Os resultados obtidos, por meio de gráficos, mostram que os modelos de dinâmica populacional (Verhulst e Malthus podem ser aplicados na modelagem da produção e importação de gás natural do Brasil.

  11. Um estudo do circuito elétrico atmosférico global

    OpenAIRE

    Turquetti, Gabriela Nunes; Ferreira, Ivan Soares

    2016-01-01

    O Circuito Elétrico Global é um modelo eletrodinâmico que tenta relacionar os mecanismos de geração de corrente na baixa atmosfera, na ionosfera e na magnetosfera. Com este modelo é possível explicar, por exemplo, os eventos atmosféricos transientes que ocorrem entre o topo da nuvem e a ionosfera logo depois de uma descarga elétrica causada pelos raios durante uma tempestade. O modelo de circuito proposto é baseado em um circuito RC, alimentado por fontes DC e AC, onde os elementos que repres...

  12. A neuro approach to solve fuzzy Riccati differential equations

    Energy Technology Data Exchange (ETDEWEB)

    Shahrir, Mohammad Shazri, E-mail: mshazri@gmail.com [InstitutSainsMatematik, Universiti Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur (Malaysia); Telekom Malaysia, R& D TM Innovation Centre, LingkaranTeknokrat Timur, 63000 Cyberjaya, Selangor (Malaysia); Kumaresan, N., E-mail: drnk2008@gmail.com; Kamali, M. Z. M.; Ratnavelu, Kurunathan [InstitutSainsMatematik, Universiti Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur (Malaysia)

    2015-10-22

    There are many applications of optimal control theory especially in the area of control systems in engineering. In this paper, fuzzy quadratic Riccati differential equation is estimated using neural networks (NN). Previous works have shown reliable results using Runge-Kutta 4th order (RK4). The solution can be achieved by solving the 1st Order Non-linear Differential Equation (ODE) that is found commonly in Riccati differential equation. Research has shown improved results relatively to the RK4 method. It can be said that NN approach shows promising results with the advantage of continuous estimation and improved accuracy that can be produced over RK4.

  13. Incorporando graus de liberdade de rotação em modelo clássico em acidente de trânsito – modelo de espalhamento entre dois veículos

    Directory of Open Access Journals (Sweden)

    Flávio Henrique Severino Oliveira Vieira

    2011-09-01

    Full Text Available Neste trabalho é apresentado de modo detalhado um modelo simples onde é introduzido o grau de liberdade de rotação no espalhamento de dois veículos, sendo o modelo clássico apresentado em detalhes na referência [3]. São adotadas aqui as mesmas aproximações não rigorosas quanto a dissipação de energia e momento no vértice de interação entre os veículos, sendo este efeito desconsiderado. O objetivo principal é o estabelecimento de um modelo com a incorporação de graus de liberdade de rotação para comparação do seu efeito frente ao modelo de “pontos materiais” usualmente empregado. Tal modelo aqui apresentado encontra-se em trabalho de formação de curso da ACADEPOL São Paulo do ano de 2006 de autoria do presente.

  14. Sistema fuzzy para estimativa do bem-estar de matrizes pesadas System fuzzy for estimate of welfare of broiler breeders

    Directory of Open Access Journals (Sweden)

    Danilo F. Pereira

    2008-12-01

    Full Text Available Entender o comportamento e suas pequenas variações decorrentes das mudanças do ambiente térmico e desenvolver modelos que simulem o bem-estar a partir de respostas das aves ao ambiente constituem o primeiro passo para a criação de um sistema de monitoramento digital de aves em galpões de produção. Neste trabalho, foi desenvolvido um sistema de suporte à decisão com base na teoria dos conjuntos fuzzy para a estimativa do bem-estar de matrizes pesadas em função de frequências e duração dos comportamentos expressos pelas aves. O desenvolvimento do sistema passou por cinco etapas distintas: 1 organização dos dados experimentais; 2 apresentação dos vídeos em entrevista com "especialista"; 3 criação das funções de pertinência com base nas entrevistas e na revisão da literatura; 4 simulação de frequências de ocorrências e tempos médios de expressão dos comportamentos classificados como indicadores de bem-estar utilizando equações de regressão obtidas na literatura, e 5 construção das regras, simulação e validação do sistema. O sistema fuzzy desenvolvido estimou satisfatoriamente o bem-estar de matrizes pesadas, tendo na sua última versão, com maior número de regras, acertado 77,8% dos dados experimentais, comparados com as respostas esperadas por um especialista. O sistema pode ser utilizado como instrumento matemático-computacional para apoiar decisões em galpões de produção de matrizes pesadas.To understand the behavior and its small variations originated from changes of the thermal environment and to develop models that simulate the welfare from broiler breeders' answer to the environment constitute the first step for the creation of a system for digital monitoring of broiler breeders housing. In this paper it was developed a system of decision support based on the fuzzy conjuncts' theory to estimate the welfare of broiler breeders in function of frequencies and times of occurrences of behaviors. The

  15. Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

    Science.gov (United States)

    Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.

    2015-01-01

    The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and

  16. Seleção de um modelo de referência de PDP para uma empresa de autopeças através de um método de auxílio à decisão por múltiplos critérios Selection of a PDP reference model to an auto parts company by means of multi-criteria techniques

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    Jefferson Olegário de Paula

    2012-01-01

    Full Text Available O processo de desenvolvimento de produtos (PDP é um fator crítico de sucesso no lançamento de um novo produto para atender os requisitos dos clientes. A adoção de um modelo de referência serve como um guia detalhado para auxiliar as organizações na gestão e sistematização do PDP, através da sequência padronizada de etapas que são adaptadas de acordo com o tipo de produto que a empresa desenvolve. O objetivo deste artigo é selecionar um modelo de referência do processo de desenvolvimento de produtos para uma empresa do ramo de autopeças através de um método de auxílio à decisão por multicritério denominado Analytic Hierarchy Process (AHP. O método de pesquisa utilizado foi a modelagem, que diante de seis alternativas, três critérios e julgamento de cinco especialistas resultou na seleção do modelo de Rozenfeld et al. (2006 como o modelo mais indicado para realizar a análise do PDP da empresa objeto de estudo em pesquisas futuras.The Product Development Process (PDP is a critical success factor in launching a new product to meet customer requirements. The adoption of a reference model serves as a comprehensive guide to assist organizations in managing and systematization of the PDP, through standardized sequence of steps that are adapted to the type of product that the company develops. The aim of this paper is to select a reference model of the product development process for the auto part sector by establishing an aid to multi-criteria decision known as Analytic Hierarchy Process (AHP. The research method used was modeling, which before six alternatives, three criteria and trial of five experts resulted in the selection of Rozenfeld et al. (2006 reference model as the most suitable model to analyze the company's PDP selected as object of study in future research.

  17. Gestão do pré-desenvolvimento de produto: estudo de casos na indústria de equipamentos médico-hospitalares Management of fuzzy front end: case studies in medical device industry

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    Glauco Henrique de Sousa Mendes

    2012-08-01

    Full Text Available O pré-desenvolvimento engloba as primeiras etapas do processo de desenvolvimento de novos produtos, nas quais as decisões são mais estratégicas e tomadas, geralmente, com alto grau de incerteza. O objetivo é analisar e discutir as práticas de gestão do pré-desenvolvimento em um conjunto de empresas de pequeno e médio portes da indústria de equipamentos médico-hospitalares. A análise é baseada num modelo conceitual, desenvolvido a partir da revisão bibliográfica, composto por cinco dimensões de gestão: orientação estratégica; processo; organização; avaliação; e ferramentas. A pesquisa de campo indica que as empresas estudadas apresentam deficiências na adoção e estruturação de boas práticas de gestão do pré-desenvolvimento. O modelo conceitual proposto serve de base para análise, estruturação e melhoria das atividades e do desempenho do pré-desenvolvimento.The fuzzy front end encompasses the first stages of new product development process. In that phase and period, the decisions are more strategic and are usually made with a high degree of uncertainty. This article aims to analyze and discuss the fuzzy front end management practices in small and medium medical device companies. The analysis is based on a conceptual model that was developed from the literature review and it consists of five dimensions of management: strategic orientation, process, organization, evaluation and tools. The results provide evidence that the studied companies have deficiencies in the adoption of best practices for managing the fuzzy front end, and the conceptual model suggested could serve as a basis for analysis, structuring and improving the fuzzy front end activities and performance.

  18. Qualidade de vida de adolescentes modelos profissionais

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    Luciana Pires

    Full Text Available Muitas adolescentes deixam suas famílias para seguir a profissão de modelo, sonhando com um futuro glamoroso. O presente estudo caso-controle analisou a qualidade de vida de 74 adolescentes do sexo feminino, sendo 37 modelos, agenciadas em São Paulo, com delineamento transversal, utilizando o World Health Organization Quality of Life - versão breve (WHOQOL-BREF, que avalia qualidade de vida global e os domínios físico, psicológico, social e ambiental. Utilizou-se o Critério Brasil 2008, para avaliação do nível socioeconômico e para parear o grupo controle. Em geral, o grupo de modelos obteve médias superiores ao grupo de não modelos, sendo esta diferença significante apenas no domínio psicológico. Observou-se que as adolescentes modelos apresentaram uma qualidade de vida semelhante à das não modelos.

  19. Um modelo para orientação familial em oligofrenias A model for familial orientation on mental retardation

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    Aguinaldo Gonçalves

    1976-03-01

    Full Text Available Após revisão de teorias e evidências sobre a etiologia das oligofrenias, são apresentados casos clínicos de oligofrênicos atendidos em serviço de Genética Médica, os quais servem de substrato para a proposição de um modelo de Aconselhamento Genético, que os classifica em ambiental, gênica, cromossômica, familial e idiopática.Theories and evidences on etiology of mental retardation are presented. Five cases of mental retardation from a Clinical Genetics Service are reported, as basis for an etiological model on mental retardation, quite operational in genetic counseling and familial orientation.

  20. Metadecisão no modelo de gestão toyotista

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    Alvair Silveira Torres Júnior

    2010-12-01

    Full Text Available O processo decisório no modelo de produção enxuta aparece como um fator pouco explorado na literatura. Os elementos que compõem o processo decisório aparecem marginalmente quando se discutem fatores como just-in-time, as parcerias com fornecedores, o desenvolvimento de produtos e a qualidade embutida no processo. O objetivo desta pesquisa é explorar como o processo decisório se apresenta no modelo de produção enxuta. Foram desenvolvidas entrevistas em profundidade com três ex-executivos do primeiro e médio escalão da subsidiária brasileira da Toyota Motor. Os entrevistados foram escolhidos intencionalmente e por oportunidade. Os resultados apresentam um conjunto de elementos do modelo de produção enxuta, denominados na literatura processos de solução de problemas, organizados em uma disposição própria, aqui reconhecida como uma metadecisão, a qual estrutura de forma peculiar o processo decisório do modelo Toyota. A contribuição deste trabalho é tornar explícita a estrutura de metadecisão do modelo de produção enxuta.

  1. Análise de um modelo para a formação de professores e suas aplicações Analysis of teachers' formation model with applications

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    Edna Maria Querido de Oliveira Chamon

    2012-09-01

    Full Text Available O objetivo deste artigo é aplicar um modelo de análise aos processos de formação docente. Este modelo é construído a partir de três polos - o conhecimento, a situação e o sujeito -, que definem três lógicas de formação: epistêmica, socioprofissional e psicológica. O modelo foi aplicado em dois estudos, uma formação continuada e uma formação inicial. Para o primeiro estudo, 189 professores da rede estadual de ensino do Estado de São Paulo avaliaram a formação que cursavam. Para o segundo estudo, 964 alunos do curso de Pedagogia de uma instituição do Estado do Pará responderam a um questionário sobre a formação recebida. Os resultados obtidos mostraram que: 1 a formação ofertada no primeiro estudo relaciona-se fundamentalmente ao polo socioprofissional do modelo; 2 a formação do segundo estudo relaciona-se aos polos socioprofissional e epistêmico.This paper proposes an analytical model to the teacher education process. The theoretical framework supporting our analysis is based on an analytical model, composed by conceptual, psychological, and professional elements, which guides the education design. This model was applied to two study cases. In the first one, 189 teachers from Sao Paulo State evaluated a continuing education program. In the second study, an education undergraduate course was evaluated by 964 students. Results showed that: 1 for the first case, the education program was mainly related to the professional axis of the model; 2 for the second case, the undergraduate course was related to both axes, professional and conceptual.

  2. Cadeia de valor da saúde: um modelo para o sistema de saúde brasileiro Healthcare value chain: a model for the Brazilian healthcare system

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    Marcelo Caldeira Pedroso

    2012-10-01

    Full Text Available Este artigo apresenta um modelo de cadeia de valor da saúde que representa, de maneira esquemática, o sistema de saúde do Brasil. O modelo proposto tem como intuito apresentar uma adequação à realidade brasileira, bem como abrangência e flexibilidade para utilização em atividades acadêmicas e análises do setor de saúde do Brasil. O modelo coloca ênfase em três componentes: principais atividades dessa cadeia, agrupadas em elos verticais e horizontais; missão de cada um desses elos; e principais fluxos da cadeia. A cadeia proposta é formada por seis elos verticais e três horizontais, perfazendo um total de nove: desenvolvimento de conhecimento em saúde; fornecimento de produtos e tecnologias; serviços de saúde; intermediação financeira; financiamento da saúde; consumo de saúde; regulação; distribuição de produtos de saúde; e serviços de apoio e complementares. A análise da cadeia proposta pode ser realizada por meio de quatro fluxos: inovação e conhecimento; produtos e serviços; financeiro; e de informação.This article presents a model of the healthcare value chain which consists of a schematic representation of the Brazilian healthcare system. The proposed model is adapted for the Brazilian reality and has the scope and flexibility for use in academic activities and analysis of the healthcare sector in Brazil. It places emphasis on three components: the main activities of the value chain, grouped in vertical and horizontal links; the mission of each link and the main value chain flows. The proposed model consists of six vertical and three horizontal links, amounting to nine. These are: knowledge development; supply of products and technologies; healthcare services; financial intermediation; healthcare financing; healthcare consumption; regulation; distribution of healthcare products; and complementary and support services. Four flows can be used to analyze the value chain: knowledge and innovation; products and

  3. Um modelo de análise envoltória de dados para o estabelecimento de metas de continuidade do fornecimento de energia elétrica

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    José Francisco Moreira Pessanha

    2007-04-01

    Full Text Available Entre os aspectos da qualidade do fornecimento de energia elétrica destaca-se a continuidade, avaliada com base nos indicadores DEC e FEC que expressam, respectivamente, a duração e a freqüência das interrupções do fornecimento. Propõe-se uma nova implementação da regulação por comparação de desempenho na definição dos níveis toleráveis de DEC/FEC (metas de continuidade para as concessionárias de distribuição e seus conjuntos de unidades consumidoras. Na abordagem proposta combinam-se dois modelos de Análise Envoltória de Dados (DEA em um processo com dois estágios: primeiro um modelo DEA clássico estabelece quanto cada distribuidora deve melhorar globalmente os seus indicadores de continuidade, em seguida, por meio de um modelo para alocação de recursos, baseado em DEA, comparam-se os desempenhos dos conjuntos em uma mesma distribuidora e definem-se as metas locais de continuidade para cada conjunto. Apresentam-se metas locais para os conjuntos das duas principais concessionárias que atendem o Estado do Rio de Janeiro.The main dimension of the electricity quality is the supply continuity. It is evaluated by indices SAIDI (System Average Interruption Duration Index and SAIFI (System Average Interruption Frequency Index. This paper presents a new implementation of the yardstick competition that combines two Data Envelopment Analysis models (DEA to set the continuity standards for the electricity distribution utilities and their groups of consumption units. The approach has two stages. First, a classical DEA model performs a comparative analysis between utilities to define global continuity standards for each utility; next, based on global standards, a model for resource allocation based on DEA establishes the local continuity standards for groups of consumption units in the same utility. Local standards for the consumption units groups of the main distribution utilities in the Rio de Janeiro State are presented.

  4. Proposta de um modelo conceitual para análise do sucesso de projetos de transferência de tecnologia: estudo em empresas farmacêuticas

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    Takahashi Vânia Passarini

    2002-01-01

    Full Text Available A aquisição de conhecimentos tecnológicos externos tem se tornado uma prática comum para as empresas farmacêuticas desenvolverem medicamentos mais eficazes e seguros. Visando colaborar para o maior conhecimento sobre o sucesso da transferência de tecnologia nessas empresas, foi revisado o tema fatores de sucesso na transferência de tecnologia e analisada a aplicabilidade de um modelo conceitual em três empresas no Canadá.

  5. A Hybrid Approach Based on the Combination of Adaptive Neuro-Fuzzy Inference System and Imperialist Competitive Algorithm: Oil Flow Rate of the Wells Prediction Case Study

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    Shahram Mollaiy Berneti

    2013-04-01

    Full Text Available In this paper, a novel hybrid approach composed of adaptive neuro-fuzzy inference system (ANFIS and imperialist competitive algorithm is proposed. The imperialist competitive algorithm (ICA is used in this methodology to determine the most suitable initial membership functions of the ANFIS. The proposed model combines the global search ability of ICA with local search ability of gradient descent method. To illustrate the suitability and capability of the proposed model, this model is applied to predict oil flow rate of the wells utilizing data set of 31 wells in one of the northern Persian Gulf oil fields of Iran. The data set collected in a three month period for each well from Dec. 2002 to Nov. 2010. For the sake of performance evaluation, the results of the proposed model are compared with the conventional ANFIS model. The results show that the significant improvements are achievable using the proposed model in comparison with the results obtained by conventional ANFIS.

  6. Proposta de um sistema de suporte executivo para micro e pequenas empresas fundamentado no modelo campos e armas da competição

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    Luciano Silva Gomes

    2010-11-01

    Full Text Available Este artigo objetiva apresentar uma proposta de SSE (Sistema de Suporte Executivo para orientar a formulação de estratégias competitivas para MPE's (Micro e Pequenas Empresas. Para isso, utilizou-se os métodos de pesquisa bibliográfica, por meio do estudo da dissertação de Gomes (2007, que especificou um SI (Sistema de Informação para formulação de estratégias competitivas através do modelo CAC (Campos e Armas da Competição, de Contador (1996, além da engenharia de software para projetar o sistema proposto. Neste trabalho foi constatado que administração estratégica nessas empresas não ocorre de um modo estruturado. Com o objetivo de formalizar este tipo de administração nestas empresas, caracterizando o problema desta pesquisa, este artigo contribui ao projetar um sistema de nível estratégico através da apresentação dos requisitos e funcionalidades necessários para o desenvolvimento de um sistema computacional.

  7. Um modelo de referência para o processo de desenvolvimento de produtos de empresas do setor moageiro de trigo A reference model for the products development process for companies in the sector of milling wheat

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    Samanta Ullmann de Campos

    2011-09-01

    Full Text Available Este artigo apresenta um modelo de referência para o processo de desenvolvimento de produtos (PDP no setor moageiro de trigo. O modelo objetiva facilitar os projetos de novos produtos, avaliando necessidades, requisitos e limitações. Ele pode contribuir para a integração e comunicação entre as áreas funcionais, reduzindo o tempo de desenvolvimento. A estrutura operacional do modelo é composta por três macrofases e oito fases. No término de cada fase, as informações são compiladas em forma de um documento, que deve passar pela avaliação da direção (gate. A aplicação do modelo foi realizada a partir de um plano para sistematizar o PDP de uma empresa. A intervenção para ajustar o modelo à empresa contemplou: análise da situação atual do PDP, proposição de melhorias e aplicação do modelo. Entre os resultados, destaca-se a facilidade de enquadrar as soluções propostas para melhorar o PDP ao modelo referencial, mostrando a exequibilidade do mesmo.This paper presents a reference model for the milling wheat product development process (PDP. The model targets to facilitate the project of new products, evaluating necessities, requirements and limitations. It contributes to integrate and create communication among the functional areas, reducing development times. The operational structure of the model is composed of three macro-phases and eight phases. At the end of each phase, the main information is compiled in document type-form, which must pass by a top management evaluation gate. The application of the reference model was tested from a plan to systematize the PDP of a company. The intervention to adjust the reference model to the company analyzes the actual PDP status, proposition of improvements, and the conduction of such application. Among the results, the facility to apply the proposed solutions to improve the company's PDP using the reference model stand out, showing the practical adequacy of the proposed model.

  8. Modelos lineares e não lineares da curva de Phillips para previsão da taxa de inflação no Brasil

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    Elano Ferreira Arruda

    2011-09-01

    Full Text Available Este trabalho compara previsões da taxa de inflação mensal brasileira a partir de diferentes modelos lineares e não lineares de séries temporais e da curva de Phillips. Em geral, os modelos não lineares apresentaram um melhor desempenho preditivo. Um modelo VAR produziu o menor erro quadrático médio de previsão (EQM entre os modelos lineares, enquanto as melhores previsões, entre todos os modelos, foram geradas pela curva de Phillips ampliada com threshold, a qual apresentou um EQM 20% menor do que a do modelo VAR. Essa diferença é significante de acordo com o teste de Diebold e Mariano (1995.

  9. Um sistema para o ensino e aprendizagem de algoritmos utilizando um companheiro de aprendizagem colaborativo

    OpenAIRE

    Petry, Patrícia Gerent

    2005-01-01

    Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-graduação em Ciência da Computação Esta dissertação apresenta um modelo computacional de um sistema de suporte ao ensino e aprendizagem no domínio de algoritmos. O sistema inclui um companheiro de aprendizagem virtual, que utiliza a Modelagem Baseada em Restrições (Constraint-Based Modelling) como forma de representação do conhecimento e raciocínio. Os Sistemas Companheiro de Aprendizagem (...

  10. Prediction on carbon dioxide emissions based on fuzzy rules

    Science.gov (United States)

    Pauzi, Herrini; Abdullah, Lazim

    2014-06-01

    There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

  11. A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

    Science.gov (United States)

    Gholami, V.; Khaleghi, M. R.; Sebghati, M.

    2017-11-01

    The process of water quality testing is money/time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CANFIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the pre-processor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study's results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low.

  12. Reliable prediction of heat transfer coefficient in three-phase bubble column reactor via adaptive neuro-fuzzy inference system and regularization network

    Science.gov (United States)

    Garmroodi Asil, A.; Nakhaei Pour, A.; Mirzaei, Sh.

    2018-04-01

    In the present article, generalization performances of regularization network (RN) and optimize adaptive neuro-fuzzy inference system (ANFIS) are compared with a conventional software for prediction of heat transfer coefficient (HTC) as a function of superficial gas velocity (5-25 cm/s) and solid fraction (0-40 wt%) at different axial and radial locations. The networks were trained by resorting several sets of experimental data collected from a specific system of air/hydrocarbon liquid phase/silica particle in a slurry bubble column reactor (SBCR). A special convection HTC measurement probe was manufactured and positioned in an axial distance of 40 and 130 cm above the sparger at center and near the wall of SBCR. The simulation results show that both in-house RN and optimized ANFIS due to powerful noise filtering capabilities provide superior performances compared to the conventional software of MATLAB ANFIS and ANN toolbox. For the case of 40 and 130 cm axial distance from center of sparger, at constant superficial gas velocity of 25 cm/s, adding 40 wt% silica particles to liquid phase leads to about 66% and 69% increasing in HTC respectively. The HTC in the column center for all the cases studied are about 9-14% larger than those near the wall region.

  13. Contribuições a estudos biológicos com o uso de modelos biofísicos

    OpenAIRE

    de Cássia Moura do Nascimento, Rita

    2004-01-01

    Um modelo biofísico é uma representação simplificada e/ou abstrata de processos ou sistemas biológicos. Objetivando ampliar o conhecimento sobre a modelagem biofísica, esta Tese enfoca prioritariamente os modelos que desenvolvemos, visando contribuir com os estudos biológicos. Proteínas transportadoras do tipo canal iônico encontram-se presentes na membrana plasmática de todos os seres vivos e o primeiro modelo biofísico é uma membrana plasmática artificial, na qual há um co...

  14. Development of a noise prediction model based on advanced fuzzy approaches in typical industrial workrooms.

    Science.gov (United States)

    Aliabadi, Mohsen; Golmohammadi, Rostam; Khotanlou, Hassan; Mansoorizadeh, Muharram; Salarpour, Amir

    2014-01-01

    Noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. One of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, advanced fuzzy approaches were employed to develop relatively accurate models for predicting noise in noisy industrial workrooms. The data were collected from 60 industrial embroidery workrooms in the Khorasan Province, East of Iran. The main acoustic and embroidery process features that influence the noise were used to develop prediction models using MATLAB software. Multiple regression technique was also employed and its results were compared with those of fuzzy approaches. Prediction errors of all prediction models based on fuzzy approaches were within the acceptable level (lower than one dB). However, Neuro-fuzzy model (RMSE=0.53dB and R2=0.88) could slightly improve the accuracy of noise prediction compared with generate fuzzy model. Moreover, fuzzy approaches provided more accurate predictions than did regression technique. The developed models based on fuzzy approaches as useful prediction tools give professionals the opportunity to have an optimum decision about the effectiveness of acoustic treatment scenarios in embroidery workrooms.

  15. Privação de sono REM em um modelo experimental da doença de Parkinson

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    Luiz A. F. Andrade

    1987-09-01

    Full Text Available Investigação prévia mostrou que ratos privados de sono (REM SD mostram acentuação de resposta a agonistas dopaminérgicos. As evidências indicam que essa ação parece ser mediada por supersensibilização de receptores dopaminérgicos pós-sinápticos. Com base nisso, foi feita REM SD em ratos com modelo experimental da doença de Parkinson, nos quais foi feita lesão eletrolítica bilateral de ambas as vias nigro-estriatais. Sete dias após a cirurgia os animais eram submetidos a REM SD por 72 horas. Imediatamente após o final deste período era feita observação em campo aberto para a ambulação, "rearing", "grooming" e latência. Em comparação com ratos não-privados foi observado aumento significativo na ambulação e "rearing", resposta que reapareceu após um segundo período de REM SD, realizado 21 dias após a cirurgia. Estes dados, de melhora de dois parâmetros de modelo experimental da doença de Parkinson, sugerem que a privação de sono pode ser útil nesta doença.

  16. APLICAÇÃO DO MÉTODO DE CLASSIFICAÇÃO CONTÍNUA FUZZY PARA O MAPEAMENTO DA FRAGILIDADE DO TERRENO EM RELAÇÃO À OCORRÊNCIA DE RAVINAS NO PARQUE NACIONAL DA SERRA DA CANASTRA - APPLICATION OF CONTINUOUS FUZZY CLASSIFICATION METHOD FOR MAPPING TERRAIN FRAGILITY REGARDING THE OCCURRENCE OF GULLIES IN THE SERRA DA CANASTRA NATIONAL PARK

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    Cassiano Gustavo Messias

    2017-04-01

    Full Text Available Este trabalho tem como objetivo realizar o mapeamento da fragilidade ambiental à ocorrência de ravinas no Parque Nacional da Serra da Canastra, por meio de um modelo espacial baseado em lógica fuzzy, desenvolvido para SIG, que combina mapas das seguintes variáveis geoambientais: índice de vegetação, densidade de lineamentos estruturais, densidade de vias de circulação e declividade do terreno. Este modelo utiliza parâmetros do teste estatístico Kolmogorov-Smirnov (KS, extraídos a partir da avaliação do grau de aderência entre a distribuição dos valores das variáveis ambientais e a distribuição espacial real das ravinas observadas no parque. Os pesos são utilizados em um processo de álgebra de mapas, baseado na soma ponderada das variáveis geoambientais convertidas em escala fuzzy. O mapa de fragilidade ambiental produzido é representado segundo o método de classificação contínua fuzzy, indicando o grau de afinidade de cada pixel à ocorrência de ravinas. Os resultados mostraram que a ordem de importância das variáveis na ocorrência de ravinas, nesta Unidade de Conservação, é a seguinte: (1 densidade de lineamentos estruturais; (2 índice de vegetação; (3 densidade de estradas; (4 declividades. As áreas de maior fragilidade concentram-se em bacias localizadas a noroeste e na porção central do Chapadão da Babilônia. Os graus menores de fragilidade são observados, sobretudo, no Chapadão da Canastra e em pontos a sudeste do Chapadão da Babilônia, em bacias que apresentam densidades de ravinas abaixo da média do parque. ABSTRACT The aim of this work was to map environmental fragility of Serra da Canastra National Park, located in Southwest region of Minas Gerais state, Brazil, using ravine sites identified on high spatial resolution images, as terrain true controls points. The methodology used for mapping was based on fuzzy logic model and GIS techniques. The model combines four environmental variables

  17. Fuzzy model to estimate the number of hospitalizations for asthma and pneumonia under the effects of air pollution.

    Science.gov (United States)

    Chaves, Luciano Eustáquio; Nascimento, Luiz Fernando Costa; Rizol, Paloma Maria Silva Rocha

    2017-06-22

    Predict the number of hospitalizations for asthma and pneumonia associated with exposure to air pollutants in the city of São José dos Campos, São Paulo State. This is a computational model using fuzzy logic based on Mamdani's inference method. For the fuzzification of the input variables of particulate matter, ozone, sulfur dioxide and apparent temperature, we considered two relevancy functions for each variable with the linguistic approach: good and bad. For the output variable number of hospitalizations for asthma and pneumonia, we considered five relevancy functions: very low, low, medium, high and very high. DATASUS was our source for the number of hospitalizations in the year 2007 and the result provided by the model was correlated with the actual data of hospitalization with lag from zero to two days. The accuracy of the model was estimated by the ROC curve for each pollutant and in those lags. In the year of 2007, 1,710 hospitalizations by pneumonia and asthma were recorded in São José dos Campos, State of São Paulo, with a daily average of 4.9 hospitalizations (SD = 2.9). The model output data showed positive and significant correlation (r = 0.38) with the actual data; the accuracies evaluated for the model were higher for sulfur dioxide in lag 0 and 2 and for particulate matter in lag 1. Fuzzy modeling proved accurate for the pollutant exposure effects and hospitalization for pneumonia and asthma approach. Prever o número de internações por asma e pneumonia associadas à exposição a poluentes do ar no município em São José dos Campos, estado de São Paulo. Trata-se de um modelo computacional que utiliza a lógica fuzzy baseado na técnica de inferência de Mamdani. Para a fuzzificação das variáveis de entrada material particulado, ozônio, dióxido de enxofre e temperatura aparente foram consideradas duas funções de pertinência para cada variável com abordagem linguísticas: bom e ruim. Para a variável de saída número interna

  18. Aplicação de um Modelo Reológico Não-Linear em Sistemas Poliméricos Dilatantes Application of Non-Linear Rheological Model to Shear-Tickening Polymeric Systems

    Directory of Open Access Journals (Sweden)

    Rômulo F. Navarro

    2002-01-01

    Full Text Available A dilatância é um comportamento reológico comum a vários sistemas poliméricos embora não ocorra em polímeros puros. Existem várias teorias aplicáveis à explicação desse fenômeno e alguns modelos matemáticos para o ajuste de dados experimentais de quatro de suas seis formas de ocorrência. Neste trabalho é dado enfoque à dilatância antecedida por um comportamento newtoniano e sucedida pelo comportamento pseudoplástico. Para ajustar dados constantes da literatura para sistemas desse tipo, é apresentado um modelo híbrido e não-linear. Para o teste do modelo foram utilizados dados existentes na literatura para solução de alfa,ômega poli(t-butilestireno em óleo mineral e suspensão de partículas do copolímero de estireno com acrilato de metila em solução aquosa de poli(ácido acrílico. O modelo proposto apresenta boa concordância com os dados experimentais.Shear-thickening (dilatant behavior is common in several polymeric systems, but not in pure polymers. There are many theories that can be used to describe shear-thickening and some mathematical models that fit experimental data of three of its six types: dilatant, newtonian-dilatant, newtonian-dilatant-pseudoplastic, viscoplastic-dilatant, pseudoplastic-dilatant and newtonian-pseudoplastic-newtonian-dilatant sequences. This work deals with the behavior of newtonian-dilatant-pseudoplastic sequence behavior and proposes a hybrid, non-linear and viscous model to fit the experimental data from systems that present such rheological behavior. The systems under investigation were: a solution of alpha,omega poly(t-butylstyrene in a mineral oil (3g/dl (sample 1 and b suspension of styrene -- methyl acrylate copolymer (S-co-MA particles (15% vol. in a poly(acrylic acid (PAA (1% wt and water solution (sample 2. The proposed model agrees with the experimental data analysed.

  19. Transformações no modelo industrial, "novos" trabalhos e nova temporalidade

    OpenAIRE

    Aquino,Cássio Adriano Braz de

    2007-01-01

    O presente artigo visa à análise da transformação da temporalidade, como elemento chave para a compreensão das mudanças no mundo do trabalho. Tomamos como referente dessa análise as teorias dos tempos sociais e a idéia do tempo dominante na constituição dos quadros temporais das sociedades. A passagem de um modelo de temporalidade relativamente estável e quase hegemônica do modelo industrial, para um tempo cada vez mais diversificado e diluído, advindo das novas jornadas - com durações e ritm...

  20. Design of an expert system based on neuro-fuzzy inference analyzer for on-line microstructural characterization using magnetic NDT method

    International Nuclear Information System (INIS)

    Ghanei, S.; Vafaeenezhad, H.; Kashefi, M.; Eivani, A.R.; Mazinani, M.

    2015-01-01

    Tracing microstructural evolution has a significant importance and priority in manufacturing lines of dual-phase steels. In this paper, an artificial intelligence method is presented for on-line microstructural characterization of dual-phase steels. A new method for microstructure characterization based on the theory of magnetic Barkhausen noise nondestructive testing method is introduced using adaptive neuro-fuzzy inference system (ANFIS). In order to predict the accurate martensite volume fraction of dual-phase steels while eliminating the effect and interference of frequency on the magnetic Barkhausen noise outputs, the magnetic responses were fed into the ANFIS structure in terms of position, height and width of the Barkhausen profiles. The results showed that ANFIS approach has the potential to detect and characterize microstructural evolution while the considerable effect of the frequency on magnetic outputs is overlooked. In fact implementing multiple outputs simultaneously enables ANFIS to approach to the accurate results using only height, position and width of the magnetic Barkhausen noise peaks without knowing the value of the used frequency. - Highlights: • New NDT system for microstructural evaluation based on MBN using ANFIS modeling. • Sensitivity of magnetic Barkhausen noise to microstructure changes of the DP steels. • Accurate prediction of martensite by feeding multiple MBN outputs simultaneously. • Obtaining the modeled output without knowing the amount of the used frequency

  1. Design of an expert system based on neuro-fuzzy inference analyzer for on-line microstructural characterization using magnetic NDT method

    Energy Technology Data Exchange (ETDEWEB)

    Ghanei, S., E-mail: Sadegh.Ghanei@yahoo.com [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of); Vafaeenezhad, H. [Centre of Excellence for High Strength Alloys Technology (CEHSAT), School of Metallurgical and Materials Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran (Iran, Islamic Republic of); Kashefi, M. [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of); Eivani, A.R. [Centre of Excellence for High Strength Alloys Technology (CEHSAT), School of Metallurgical and Materials Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran (Iran, Islamic Republic of); Mazinani, M. [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of)

    2015-04-01

    Tracing microstructural evolution has a significant importance and priority in manufacturing lines of dual-phase steels. In this paper, an artificial intelligence method is presented for on-line microstructural characterization of dual-phase steels. A new method for microstructure characterization based on the theory of magnetic Barkhausen noise nondestructive testing method is introduced using adaptive neuro-fuzzy inference system (ANFIS). In order to predict the accurate martensite volume fraction of dual-phase steels while eliminating the effect and interference of frequency on the magnetic Barkhausen noise outputs, the magnetic responses were fed into the ANFIS structure in terms of position, height and width of the Barkhausen profiles. The results showed that ANFIS approach has the potential to detect and characterize microstructural evolution while the considerable effect of the frequency on magnetic outputs is overlooked. In fact implementing multiple outputs simultaneously enables ANFIS to approach to the accurate results using only height, position and width of the magnetic Barkhausen noise peaks without knowing the value of the used frequency. - Highlights: • New NDT system for microstructural evaluation based on MBN using ANFIS modeling. • Sensitivity of magnetic Barkhausen noise to microstructure changes of the DP steels. • Accurate prediction of martensite by feeding multiple MBN outputs simultaneously. • Obtaining the modeled output without knowing the amount of the used frequency.

  2. A modified dynamic evolving neural-fuzzy approach to modeling customer satisfaction for affective design.

    Science.gov (United States)

    Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael

    2013-01-01

    Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

  3. Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

    Directory of Open Access Journals (Sweden)

    Shu-Yin Chiang

    2016-12-01

    Full Text Available Ubiquitous health care (UHC is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN. The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

  4. Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement.

    Science.gov (United States)

    Chiang, Shu-Yin; Kan, Yao-Chiang; Chen, Yun-Shan; Tu, Ying-Ching; Lin, Hsueh-Chun

    2016-12-03

    Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

  5. A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design

    Directory of Open Access Journals (Sweden)

    C. K. Kwong

    2013-01-01

    Full Text Available Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1 the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS failed to run due to a large number of inputs; (2 the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

  6. Procedimento fuzzy aplicado à avaliação da insalubridade em atividades agrícolas Fuzzy procedure applied to evaluate the insalubrity level in agriculture activities

    Directory of Open Access Journals (Sweden)

    Tadayuki Yanagi Junior

    2012-06-01

    Full Text Available Diante do alto grau de mecanização a que as atividades agrícolas estão sendo submetidas, objetivou-se, com esta pesquisa, desenvolver um modelo fuzzy capaz de avaliar e classificar o nível de insalubridade em diversos ambientes de trabalho. O modelo desenvolvido tem como variáveis de entrada: o índice de bulbo úmido e temperatura de globo (IBUTG, °C, o nível de ruído (dBA, a taxa de metabolismo (W m-2 e o tempo de descanso (% e, como variável de saída, o índice de bem-estar humano (IBEH. O método de inferência utilizado foi o de Mandani e, na defuzificacão, utilizou-se o método do centro de gravidade. O sistema de regras foi desenvolvido com base nas combinações das variáveis de entrada. Foram definidas 400 regras com pesos iguais a 1, sendo que, na elaboração das regras, um especialista da área foi consultado. Foram utilizados dados de campo visando a testar o sistema desenvolvido, e os resultados mostraram que a modelagem proposta é uma ferramenta promissora na determinação do IBEH, apresentando tempo de descanso ideal variando de 64,2% (motosserra, próximo ao ouvido do operador até 25% (derriçadora, 20 m de distância do operador, sendo que, diante de um cenário predefinido do ambiente térmico e acústico, foi possível determinar o grau de bem-estar humano e o tempo de descanso ideal para cada equipamento avaliado.Given the high degree of mechanization to which agricultural activities are being submitted, the objective of this research was to develop a fuzzy model able to evaluate and classify the insalubrity level in different work environments. The model developed has as input variables: wet bulb globe temperature index (WBGTI, °C, noise level (dBA, metabolic rate (W m-2 and rest time (%; and has as output variable the human well-being index (HWBI. The Mamdani inference method was used, and for the defuzzification, the Center of Gravity method was used. The system of rules was developed based on

  7. As guardas municipais no Brasil: Um modelo de análise

    Directory of Open Access Journals (Sweden)

    Joana Domingues Vargas

    2010-01-01

    Full Text Available Este artigo busca elaborar um modelo de análise que permita verifi car o quanto os projetos de funcionamento das guardas municipais no Brasil oscilam entre restringir-se ao seu mandato constitucional e o extravasar e, nesse mesmo movimento, identifi car que direção esses projetos estão tomando e se estão levando à constituição ou não de uma cultura organizacional própria. Partiu-se do pressuposto de que as guardas municipais podem ser avaliadas como realizações empíricas que conjugam características de três tipos de estrutura de policiamento e de estilos ou de perfis de atuação dos guardas: polícia municipal, polícia comunitária e guarda patrimomonial. City guards in Brazil: A model of analysis aims to develop a model of analysis that allows one to verify to what extent the operational projects of city guards in Brazil vary between being restricted to their constitutional mandate and transgressing that limit, and also to identify the bearing these projects are taking and whether they are leading to the constitution of their own organizational culture. The study was based on the premise that city guards can be evaluated as empirical realizations that combine features of three types of police structure and styles or profi les of the guards’ work: municipal police, community police and security guards.

  8. A Administração Estratégica do Capital Intelectual: Um Modelo Baseado na Capacidade Absortiva para Potencializar Inovação

    Directory of Open Access Journals (Sweden)

    Alessandra Cassol

    2016-04-01

    Full Text Available O capital intelectual é percebido como um recurso estratégico capaz de ser propulsor da inovação. Neste contexto, sugere-se que o capital intelectual e a capacidade absortiva podem ser influenciadores da capacidade de inovar nas organizações e propõe-se um Modelo de administração estratégica do capital intelectual. Este artigo tem como objetivo propor e analisar um Modelo de Administração Estratégica do Capital Intelectual a partir de práticas da capacidade absortiva como potencializadora de inovação. Como método utiliza-se a pesquisa exploratória com abordagem quantitativa e aplicação de questionários para uma amostra de 104 gestores. Na análise dos dados utiliza-se a técnica de modelagem de equações estruturais por meio do Partial Least Squares (PLS, com a análise dos coeficientes de caminho observa-se uma forte relação entre os constructos o que confirmou as hipóteses da pesquisa. Verificamos que as práticas organizacionais adotadas para o desenvolvimento do capital intelectual, da capacidade absortiva e da inovação são: a capacitação constante dos colaboradores; b programas de sugestões; c assimilação de novas tecnologias; d aplicação de conhecimentos técnicos; e parcerias com órgãos de apoio à inovação. Como contribuições, as evidências encontradas indicam que a capacidade absortiva promove o avanço da inovação, podendo ainda ser possível observar práticas de gestão do capital intelectual a partir de rotinas organizacionais.

  9. Levantamento do modelo de custeio de um hospital: características e utilização da informação

    OpenAIRE

    Vinhas, Susana Inês das Neves

    2015-01-01

    Mestrado em Contabilidade / Sistema de Classificação JEL: M40 – General; M41 – Accountig; M49 – Other Desde o final do Séc. XX, as técnicas de contabilidade de gestão têm evoluído a um ritmo vertiginoso (Johnson, 1995), surgindo modelos de custeio cada vez mais sofisticados que promovem o registo e alocação mais eficaz de custos, produzindo informação estratégica útil à tomada de decisões (Heitger et al., 1992; Drury, 2002; Pereira & Jorge, 2010). Ainda assim, muitas organizações como os h...

  10. Desenvolvimento de um modelo para o biospeckle na análise de sementes de feijão (Phaseolus vulgaris L. Development of a model for the biospeckle applied to analise been seed (Phaseolus vulgaris L.

    Directory of Open Access Journals (Sweden)

    Álvaro Leonardo do Nascimento

    2007-04-01

    Full Text Available O biospeckle é uma figura de interferência formada pela reflexão difusa da luz coerente espalhada ao interagir com um objeto que apresenta algum tipo de atividade, biológica ou não. O padrão de interferência se modifica ao longo do tempo devido às estruturas responsáveis pelo espalhamento estarem em atividade. Esse fenômeno tem sido estudado com o intuito de se desenvolver um método rápido e não destrutivo para avaliação de materiais biológicos. A obtenção de um modelo simples que descreva os aspectos essenciais do fenômeno é um importante passo para o domínio da técnica. Neste trabalho é apresentado um modelo para descrever a formação do biospeckle, desenvolvido a partir de hipóteses simples sobre como o tecido biológico difrata a luz coerente e qual o efeito da atividade biológica sobre a difração. Foram comparados os resultados da simulação do modelo com resultados experimentais obtidos de sementes. O modelo reproduz com sucesso algumas das características básicas do padrão dinâmico.Biospeckle is an interference pattern phenomenon formed by the diffuse reflection of coherent light scattered by any type of activity, biological or not. The interference pattern changes in time due to the movement of the structures that scatter the light. This phenomenon has been studied with the goal of developing a fast and non-destructive method for evaluating biological material. Building a simple model that describes the essential aspects of the phenomenon is an important step towards the development of this technique. In this work is presented a model that describes the formation of the biospeckle, based on simple assumptions about the scattering of coherent light by the biological material and the nature of biological activity. We compare the results provided by the model with experimental biospeckle obtained from seeds. The model reproduces successfully some of the basic characteristics of the dynamical experimental

  11. Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS

    Science.gov (United States)

    Tien Bui, Dieu; Pradhan, Biswajeet; Lofman, Owe; Revhaug, Inge; Dick, Oystein B.

    2012-08-01

    The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the prediction capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest prediction capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.

  12. Atitude empreendedora: validação de um instrumento de medida com base no modelo de resposta gradual da teoria da resposta ao item.

    Directory of Open Access Journals (Sweden)

    Antônio Cezar Bornia

    2013-10-01

    Full Text Available A escala instrumento de medida de atitude empreendedora (Imae, desenvolvida por Souza e Lopes Jr. (2005, contém duas dimensões: prospecção e inovação, e gestão e persistência. Com a finalidade de verificar a validade e o intervalo em que propicia a medida de atitude empreendedora, além de investigar sua capacidade de discriminar a resposta que o indivíduo está apto a dar, o objetivo deste artigo é validar a escala Imae por meio do modelo de resposta gradual da teoria da resposta ao item (TRI, que revolucionou a teoria de medidas. A TRI, construtos da psicologia utilizados em estudos de discriminação de respostas, em especial em grandes amostras de respondentes a um determinado fenômeno, é constituída de modelos matemáticos que relacionam um ou mais traços latentes (não observados de um indivíduo com a probabilidade de este dar uma determinada resposta a um item. O ponto crucial da TRI é que ela leva em consideração o item particularmente, sem relevar os escores totais, portanto as conclusões não dependem apenas do teste ou questionário, mas de cada elemento que o compõe. Os principais resultados encontrados foram a identificação de dois níveis da escala, denominados âncoras, que permitem interpretar tendências de pessoas com atitude empreendedora e a constatação de que os itens da escala Imae apresentam boa capacidade de discriminar a resposta que o indivíduo está apto a dar, o que confere qualidade aos itens e, portanto, à escala. A importância deste estudo reside no papel fundamental que a atitude desempenha nas escolhas que as pessoas fazem em relação à própria vida, de modo a ajudá-las a determinar seus próprios atos.

  13. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

    Science.gov (United States)

    Oh, Hyun-Joo; Pradhan, Biswajeet

    2011-09-01

    This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.

  14. Endividamento externo e controles de capitais: uma análise computacional de um modelo macrodinâmico Pós-keynesiano

    Directory of Open Access Journals (Sweden)

    José Luís Oreiro

    2006-12-01

    Full Text Available O presente artigo analisa o impacto dos controles de capitais sobre a trajetória temporal de uma série de variáveis macroeconômicas para economias em desenvolvimento a partir de um modelo pós-keynesiano de economia aberta e sem governo. Inicialmente, apresentamos um modelo macroeconômico que introduz o controle de capitais como variável de escolha de política econômica. A partir da análise de estabilidade do modelo deduzimos um conjunto de valores economicamente plausíveis para os parâmetros. Com base nestes parâmetros analisamos o comportamento dinâmico do grau de utilização da capacidade produtiva, do endividamento externo, das exportações líquidas e da taxa de juros sob diferentes condições iniciais. Na seqüência, analisamos a sensibilidade da economia a choques externos como, por exemplo, variações da taxa de juros internacional e do fluxo internacional de comércio. Nesse contexto, constatamos que os controles de capitais podem contribuir para a redução da volatilidade da dívida externa a choques exógenos, mas amplificam os efeitos desses choques sobre o grau de utilização da capacidade produtiva. Desta forma, a desejabilidade da adoção de controles de capitais é condicional às preferências da sociedade no que se refere à volatilidade do grau de utilização da capacidade vis-à-vis à volatilidade do endividamento externo.This article analyses the effects of capital controls over the time-path of several macroeconomic variables of developing economies by means of a post-Keynesian macroeconomic model for an open economy without government activities. First of all, we present a macroeconomic model that embodies capital controls as an economic policy instrument. After the stability analysis of the model, we deduce a set of economic plausible values for the parameters. Using these parameter values, we describe the dynamical behavior of capacity utilization level, external debt, net exports and interest rate

  15. Qualidade total: Um novo paradigma?

    Directory of Open Access Journals (Sweden)

    Suzana da Rosa Tolfo

    1999-01-01

    Full Text Available Nos últimos anos, o movimento para a implantação da Gestão da Qualidade Total vem crescendo ao redor do mundo. Em razão disso, há uma diversidade de ações realizadas com o nome de "Qualidade Total'. Uma revisão da teoria é complexa, porque existem muitos autores que tratam da questão. Eles escolhem diferentes perspectivas de análises (teóricas e empíricas e há dificuldades em se identificar um corpo conceitual. Há uma ampla difusão de modelos, ferramentas, técnicas, mercado e consultores. Essa popularidade, muitas vezes, faz com que determinadas organizações adotem essa forma de gestão do trabalho sem o conhecimento necessário das implicações que um modelo dessa ordem representa; especialmente no caso brasileiro, suscetível a proposições importadas. O presente artigo propõe um exame daquilo que os fundadores têm articulado sobre TQM, as principais críticas nesta direção e a avaliação de como vem sendo aplicadono nosso país.

  16. Um modelo de hipertexto para apoio ao ensino mediado pela Web

    OpenAIRE

    Willie Dresler Leiva

    2003-01-01

    Atualmente há uma demanda crescente por aplicações hipermídia baseadas na WWW (World Wide Web), conhecidas como WIS (Web Information Systems). Esse novo tipo de aplicação apresenta requisitos adicionais aos sistemas de software clássicos, o que resulta na necessidade de investigar modelos mais adequados para apoiar o seu desenvolvimento. Em especial, os sistemas para apoio ao EaD (Ensino a Distância) baseados na Web apresentam características e requisitos ainda mais específicos. Os modelos at...

  17. Modelos de engajamento

    Directory of Open Access Journals (Sweden)

    Roberval de Jesus Leone dos Santos

    2005-08-01

    Full Text Available O artigo apresenta três modelos de engajamento propostos por autores fundamentais do século XX: Gramsci, Sartre e Camus. Após a revisão dos modelos, o autor desenvolve uma abordagem generalizada do fenômeno e apresenta duas conclusões principais: o engajamento tem de ser entendido em termos de grau; o engajamento é um fenômeno exclusivo de sociedades políticas ou antagônicas.The paper presents three models of engagement proposed by fundamental authors of the 20th century: Gramsci, Sartre and Camus. After the revision of the models, the author develops a generalized approach of the phenomenon and it presents two main conclusions: the engagement must be understood within limits of degree; the engagement is a phenomenon exclusive of political or antagonistic societies.

  18. Um Termômetro para as Macro-Prudenciais

    Directory of Open Access Journals (Sweden)

    Fabio Kanczuk

    2013-12-01

    Full Text Available Desenvolvemos um modelo com fricções no crédito tanto para firmas como famílias. Crédito às firmas é tratado como nos modelos de acelerador financeiro (e.g. Bernanke e Gilchrist (1999. Os juros sobre os recursos emprestados às famílias dependem de seu endividamento, como em Curdia e Woodford (2010. O modelo é estimado para o Brasil, utilizado para estudar os episódios de desaceleração, e para a extração dos prêmios de financiamento (destilados a partir de dados não financeiros, os quais são comparados com informações sobre crédito às pessoas físicas e jurídicas. Dessa forma, o obtém-se um termômetro para mensurar como medidas prudenciais sobre o crédito afetam atividade e inflação.

  19. Recuperação de informação em dados ligados: um modelo baseado em mapas conceituais e análise de redes complexas

    Directory of Open Access Journals (Sweden)

    Henrique Monteiro CRISTOVÃO

    Full Text Available Resumo Este artigo apresenta um modelo para recuperação de informação em dados abertos ligados, usando métodos e operações de redes complexas para classificação e seleção de informações, bem como mapas conceituais para apresentação das informações recuperadas ao usuário. O modelo evidencia os relacionamentos entre os termos de consulta que representam uma necessidade informacional e os apresenta enquanto mapas conceituais. A hipótese subjacente é que o relacionamento do usuário com a informação recuperada ocorre à luz da equação fundamental da Ciência da Informação de Brookes, em que a estrutura cognitiva do cognoscente é uma rede complexa que é modulada pela informação recuperada, a qual, por sua vez, é derivada de uma rede complexa. A rede complexa final é mapeada em um mapa conceitual resultante aprimorado com heurísticas. Com características qualitativas e o emprego de abordagem exploratória, a pesquisa realizou primeiramente um teste piloto de recuperação da informação, que permitiu aferir os algoritmos empregados no ranqueamento e seleção nas redes de informação intermediárias, servindo de base para implementação de um protótipo. O protótipo empregou uma base de conhecimento de dados abertos ligados (linked open data, derivada da DBpedia, sobre a qual foram realizadas operações de análise de redes complexas, apresentando revocação e precisão relevantes, perante uma validação aplicada a um grupo de 17 usuários. Os resultados são promissores quanto ao uso de operações de redes complexas e mapas conceituais na recuperação de informação, especialmente em dados ligados. Como continuidade da pesquisa, observou-se demanda por ações mais interativas e pela realização de experimentos em outras bases de conhecimento.

  20. Business Planning in the Light of Neuro-fuzzy and Predictive Forecasting

    Science.gov (United States)

    Chakrabarti, Prasun; Basu, Jayanta Kumar; Kim, Tai-Hoon

    In this paper we have pointed out gain sensing on forecast based techniques.We have cited an idea of neural based gain forecasting. Testing of sequence of gain pattern is also verifies using statsistical analysis of fuzzy value assignment. The paper also suggests realization of stable gain condition using K-Means clustering of data mining. A new concept of 3D based gain sensing has been pointed out. The paper also reveals what type of trend analysis can be observed for probabilistic gain prediction.

  1. Simulação do comportamento mecânico de misturas asfálticas usando um modelo computacional multi-escala

    Directory of Open Access Journals (Sweden)

    Flávio Vasconcelos de Souza

    2009-10-01

    Full Text Available As misturas asfálticas, por serem materiais heterogêneos, possuem comportamento global dependente do comportamento dos constituintes individuais, de suas frações volumétricas e das interações físico-químicas entre os constituintes, dentre outros fatores. Deste modo, para que se possa compreender melhor o comportamento desses materiais, é necessário o uso de metodologias capazes de considerar as características e fenômenos ocorrentes nas escalas menores. Uma metodologia que vem sendo bastante estudada e aplicada na comunidade científica internacional são os chamados modelos multi-escala. O objetivo do presente trabalho é descrever um modelo computacional multi-escala e aplicá-lo à simulação de ensaios comumente usados em misturas asfálticas, quais sejam, os ensaios de compressão diametral e de fadiga por flexão em viga. Para o caso de compressão diametral, os resultados numéricos se mostraram em concordância com os resultados observados experimentalmente. Para o caso de carregamento cíclico, não foi feita uma comparação com experimentos, mas os resultados numéricos mostram a capacidade do modelo em simular qualitativamente os fenômenos de trincamento por fadiga e acúmulo de deformações permanentes.

  2. Dimensionamento de recursos humanos: desenvolvimento de um modelo conceitual e sua aplicação Dimensioning human resources: development and application of a conceptual model

    Directory of Open Access Journals (Sweden)

    Bernadete de Lourdes Marinho

    2007-06-01

    Full Text Available Determinar o número de ocupantes dos vários cargos e funções em uma empresa não é tarefa fácil. Com freqüência, essa tarefa é realizada com base na percepção dos chefes, negociações com a administração da empresa e tentativas sucessivas com base em erros e acertos. Qualquer dessas alternativas apresenta resultados muito questionáveis, seja do ponto de vista da empresa, seja sob a ótica dos empregados. A Empresa Municipal de Habitação (EMH é uma empresa municipal voltada para a solução de problemas de moradia das classes de baixa renda, que vem se destacando pelo uso de técnicas modernas de gerenciamento. No sentido de melhor quantificar suas necessidades de recursos humanos, a empresa, que tem investido nos últimos anos em treinamento de recursos humanos e na modernização de seus processos gerenciais, realizou um estudo com base em um modelo conceitual que possibilitou um avanço nos processos tradicionais de dimensionamento. Este trabalho inicia com uma revisão bibliográfica sobre o tema, seguida de uma breve descrição da empresa. A seguir, um modelo conceitual é apresentado e aplicado à realidade da EMH. Recomendações e conclusões sobre o método usado encerram o texto.Definition of positions and functions in a company is complex and frequently based on manager perceptions and administrative negotiations as well as trial and error. These approaches are questionable from both the company and employee perspectives. EMF is a municipal company focused on resolving low income housing problems. The company has invested in training of personnel and modernizing management. Now it has studied and adopted a conceptual model to improve traditional dimensioning for human resource staffing. A review of pertinent literature was made, followed by a description of EMF and the experience with the model. In conclusion recommendations and conclusions are made on the subject.

  3. A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

    Science.gov (United States)

    Tahmasebi, Pejman; Hezarkhani, Ardeshir

    2012-05-01

    The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.

  4. Desenvolvimento de um modelo para análise da acumulação de capacidades tecnológicas na indústria da construção civil: subsetor de edificações

    Directory of Open Access Journals (Sweden)

    Renata Furtado Gradvohl

    Full Text Available Diante das várias conceituações existentes na literatura para capacidade tecnológica em países em industrialização, neste trabalho capacidade tecnológica é entendida como os recursos necessários para gerar e administrar mudanças tecnológicas. A partir dessa definição, este estudo tem como objetivo desenvolver um modelo de análise da acumulação de capacidades tecnológicas em firmas de construção civil que atuam no subsetor de edificações. Para fins de desenvolvimento do modelo proposto, foram conduzidas uma pesquisa bibliográfica e um levantamento junto a especialistas do setor. Após essa etapa, o modelo foi validado junto a três gestores de empresas de construção. Com base na pesquisa bibliográfica, optou-se por analisar três funções tecnológicas: “Processo e Organização da Produção”, função que engloba as atividades referentes ao processo produtivo da obra; “Projeto”, em que estão inseridas as atividades relacionadas ao desenvolvimento do projeto da obra; e “Equipamentos”, que contém as atividades ligadas ao uso, aprimoramento e desenho dos equipamentos usados pela companhia no processo de produção. O modelo final pode ser utilizado para exame e gestão do processo de desenvolvimento industrial de firmas desse setor.

  5. Decomposition of fuzzy continuity and fuzzy ideal continuity via fuzzy idealization

    International Nuclear Information System (INIS)

    Zahran, A.M.; Abbas, S.E.; Abd El-baki, S.A.; Saber, Y.M.

    2009-01-01

    Recently, El-Naschie has shown that the notion of fuzzy topology may be relevant to quantum paretical physics in connection with string theory and E-infinity space time theory. In this paper, we study the concepts of r-fuzzy semi-I-open, r-fuzzy pre-I-open, r-fuzzy α-I-open and r-fuzzy β-I-open sets, which is properly placed between r-fuzzy openness and r-fuzzy α-I-openness (r-fuzzy pre-I-openness) sets regardless the fuzzy ideal topological space in Sostak sense. Moreover, we give a decomposition of fuzzy continuity, fuzzy ideal continuity and fuzzy ideal α-continuity, and obtain several characterization and some properties of these functions. Also, we investigate their relationship with other types of function.

  6. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    Directory of Open Access Journals (Sweden)

    Y.-M. Chiang

    2011-01-01

    Full Text Available Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

  7. Using fuzzy self-organising maps for safety critical systems

    International Nuclear Information System (INIS)

    Kurd, Zeshan; Kelly, Tim P.

    2007-01-01

    This paper defines a type of constrained artificial neural network (ANN) that enables analytical certification arguments whilst retaining valuable performance characteristics. Previous work has defined a safety lifecycle for ANNs without detailing a specific neural model. Building on this previous work, the underpinning of the devised model is based upon an existing neuro-fuzzy system called the fuzzy self-organising map (FSOM). The FSOM is type of 'hybrid' ANN which allows behaviour to be described qualitatively and quantitatively using meaningful expressions. Safety of the FSOM is argued through adherence to safety requirements-derived from hazard analysis and expressed using safety constraints. The approach enables the construction of compelling (product-based) arguments for mitigation of potential failure modes associated with the FSOM. The constrained FSOM has been termed a 'safety critical artificial neural network' (SCANN). The SCANN can be used for non-linear function approximation and allows certified learning and generalisation for high criticality roles. A discussion of benefits for real-world applications is also presented

  8. Gestão de políticas de cultura e qualidade da democracia: São Paulo, 10 anos de um modelo ainda em construção

    Directory of Open Access Journals (Sweden)

    José Veríssimo Romão Netto

    2015-08-01

    Full Text Available Organizações sociais vêm assumindo papel crescente na gestão das políticas públicas de cultura no governo do estado de São Paulo. Fundado na escola da chamada "nova gestão pública", pode-se afirmar que esse modelo de gestão traria mais qualidade para a democracia a partir das reformas das organizações administrativas do estado. À luz da teoria da qualidade da democracia, o artigo analisa a implementação do modelo no estado, utilizando-se de dados descritivos dos recursos aportados e dos contratos de gestão estabelecidos pelo governo com a sociedade civil. Conclui-se que, a despeito do elevado esforço na implementação do modelo, ainda não se pode notar um padrão de gestão a partir das organizações sociais de cultura e sua relação com o Estado, bem como se sugerem outras dimensões investigativas para estabelecimento de agenda de pesquisa.

  9. Ground Motion Prediction Model Using Adaptive Neuro-Fuzzy Inference Systems: An Example Based on the NGA-West 2 Data

    Science.gov (United States)

    Ameur, Mourad; Derras, Boumédiène; Zendagui, Djawed

    2018-03-01

    Adaptive neuro-fuzzy inference systems (ANFIS) are used here to obtain the robust ground motion prediction model (GMPM). Avoiding a priori functional form, ANFIS provides fully data-driven predictive models. A large subset of the NGA-West2 database is used, including 2335 records from 580 sites and 137 earthquakes. Only shallow earthquakes and recordings corresponding to stations with measured V s30 properties are selected. Three basics input parameters are chosen: the moment magnitude ( Mw), the Joyner-Boore distance ( R JB) and V s30. ANFIS model output is the peak ground acceleration (PGA), peak ground velocity (PGV) and 5% damped pseudo-spectral acceleration (PSA) at periods from 0.01 to 4 s. A procedure similar to the random-effects approach is developed to provide between- and within-event standard deviations. The total standard deviation (SD) varies between [0.303 and 0.360] (log10 units) depending on the period. The ground motion predictions resulting from such simple three explanatory variables ANFIS models are shown to be comparable to the most recent NGA results (e.g., Boore et al., in Earthquake Spectra 30:1057-1085, 2014; Derras et al., in Earthquake Spectra 32:2027-2056, 2016). The main advantage of ANFIS compared to artificial neuronal network (ANN) is its simple and one-off topology: five layers. Our results exhibit a number of physically sound features: magnitude scaling of the distance dependency, near-fault saturation distance increasing with magnitude and amplification on soft soils. The ability to implement ANFIS model using an analytic equation and Excel is demonstrated.

  10. An effective Load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system

    Directory of Open Access Journals (Sweden)

    Foday Conteh

    2017-09-01

    Full Text Available In recent years, the use of renewable energy sources in micro-grids has become an effectivemeans of power decentralization especially in remote areas where the extension of the main power gridis an impediment. Despite the huge deposit of natural resources in Africa, the continent still remains inenergy poverty. Majority of the African countries could not meet the electricity demand of their people.Therefore, the power system is prone to frequent black out as a result of either excess load to the systemor generation failure. The imbalance of power generation and load demand has been a major factor inmaintaining the stability of the power systems and is usually responsible for the under frequency andunder voltage in power systems. Currently, load shedding is the most widely used method to balancebetween load and demand in order to prevent the system from collapsing. But the conventional methodof under frequency or under voltage load shedding faces many challenges and may not perform asexpected. This may lead to over shedding or under shedding, causing system blackout or equipmentdamage. To prevent system cascade or equipment damage, appropriate amount of load must beintentionally and automatically curtailed during instability. In this paper, an effective load sheddingtechnique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system isproposed. The combined techniques take into account the actual system state and the exact amount ofload needs to be curtailed at a faster rate as compared to the conventional method. Also, this methodis able to carry out optimal load shedding for any input range other than the trained data. Simulationresults obtained from this work, corroborate the merit of this algorithm.

  11. Modelo de gerenciamento da manutenção de equipamentos de radiologia convencional

    OpenAIRE

    Couto,Nelson Fraga do; Ribeiro,Rildo Santos; Azevedo,Ana Cecília Pedrosa de; Carvalho,Antonio Carlos Pires

    2003-01-01

    Foi elaborado um modelo de gerenciamento da manutenção dos equipamentos convencionais de raios X no Hospital Universitário Clementino Fraga Filho. O modelo foi implementado em várias etapas, que incluíram visitas a centros hospitalares que gerenciam seus equipamentos, linha de colaboração com a Fundação Oswaldo Cruz para realização de cursos e treinamento de técnicos de eletrônica do hospital, montagem de uma esquemateca dos equipamentos em uso, criação de um programa de gerenciamento utiliza...

  12. Efeito do modelo de periodização com cargas seletivas sobre capacidades motoras durante um mesociclo preparatório em jogadores de futsal

    Directory of Open Access Journals (Sweden)

    Carlos Rogério Thiengo

    2013-12-01

    Full Text Available O objetivo deste estudo foi verificar o efeito do modelo de cargas seletivas durante um mesociclo preparatório em jogadores de futsal profissionais. Nove jogadores de futsal (23±2 anos de uma equipe da Federação Paulista de Futebol de Salão compuseram a amostra, caracterizada como intencional. Aptidão aeróbia intermitente, salto horizontal, salto triplo unipodal, velocidade de 15m, agilidade e potência anaeróbia foram avaliadas em dois momentos: microciclo-2 (PRE e microciclo-7 (POS. A análise estatística compreendeu o teste-t de Student e teste Wilxocon (P<0,05. Salto horizontal, aptidão aeróbia intermitente e potência média melhoraram, a velocidade nos 15 m piorou. O modelo de cargas seletivas é uma boa opção de periodização para o mesociclo preparatório no futsal.

  13. Noopsicossomática em pessoas vivendo com HIV/AIDS: evidências de um modelo explicativo = Noopsychosomatics in people living with HIV/AIDS: evidence of an explanatory model = Noopsicosomática en personas que viven con VIH/SIDA: evidencia de un modelo explicativo

    Directory of Open Access Journals (Sweden)

    Pontes, Alisson de Meneses

    2015-01-01

    Full Text Available O objetivo deste estudo foi testar um modelo teórico proposto por Viktor Frankl, que pode explicar a dinâmica da noopsicossomática em pessoas com HIV/AIDS. Especificamente, buscou-se entender como a dimensão noológica (representada pela atitude religiosa e a realização de sentido influencia a dimensão psíquica (afetos positivos e a percepção do passado e, consequentemente, repercute na dimensão somática (números de células CD4+/CD8+. Participaram 115 pacientes soropositivos para o HIV/AIDS, com idade média de 39 anos (61,4% do sexo masculino. Os resultados apontaram para a adequação deste modelo, com os índices de ajuste aceitáveis. Conclui-se que foram reunidas evidências acerca do modelo teórico da noopsicossomática em pacientes com HIV/AIDS, corroborando a relevância da dimensão noológica no processo de adoecimento e saúde

  14. Fuzzy control for optimal operation of complex chilling systems; Betriebsoptimierung von komplexen Kaelteanlagen mit Fuzzy-Control

    Energy Technology Data Exchange (ETDEWEB)

    Talebi-Daryani, R. [Fachhochschule Koeln (Germany). Lehrgebiet und Lab. fuer Regelungs- und Gebaeudeleittechnik; Luther, C. [JCI Regelungstechnik GmbH, Koeln (Germany)

    1998-05-01

    The optimization potentials for the operation of chilling systems within the building supervisory control systems are limited to abilities of PLC functions with their binary logic. The aim of this project is to replace inefficient PLC-solutions for the operation of chilling system by a Fuzzy control system. Optimal operation means: reducing operation time and operation costs of the system, reducing cooling energy generation- and consumption costs. Analysis of the thermal behaviour of the building and the chilling system is necessary, in order to find the current efficient cooling potentials and cooling methods during the operation. Three different Fuzzy controller have been developed with a total rule number of just 70. This realized Fuzzy control system is able to forecast the maximum cooling power of the building, but also to determine the cooling potential of the out door air. This new Fuzzy control system has been successfully commissioned, and remarkable improvement of the system behaviour is reached. Comparison of the system behaviour before and after the implementation of Fuzzy control system proved the benefits of the Fuzzy logic based operation system realized here. The system described here is a joint project between the University of applied sciences Cologne, and Johnson Controls International Cologne. The Fuzzy software tool used here (SUCO soft Fuzzy TECH 4.0), was provided by Kloeckner Moeller Bonn. (orig.) [Deutsch] Die Betriebsoptimierung von Kaelteanlagen innerhalb von Gebaeudeleitsystemen ist auf die Faehigkeiten von logischen Steuerverknuepfungen der Digitaltechnik begrenzt. In diesem Zusammenhang kann nur ein geringer Anteil der Information ueber das thermische Speicherverhalten des jeweiligen Gebaeudes herangezogen werden. Ziel des vorliegenden Projektes war es, die unzureichenden logischen Steuerverknuepfungen durch ein Fuzzy-Control-System zu ersetzen, um die Arbeitsweise der Kaelteanlage zu optimieren. Die Optimierungskriterien dieses

  15. Modelos de vulnerabilidade social a desastres

    Directory of Open Access Journals (Sweden)

    David Alexander

    2012-10-01

    Full Text Available Neste artigo discutem‑se as bases teóricas da avaliação da vulnerabilidade social aos desastres e mostra‑se que a vulnerabilidade é a componente mais importante do risco e o elemento principal dos impactos dos desastres. A percepção é um processo‑chave na tomada de decisões em situações de desastre, sendo afectada pela cultura e pelo simbolismo, que são analisados no contexto do risco de desastres. Recorre‑se a um modelo de metamorfose cultural para explicar as mudanças e as discrepâncias nas atitudes em relação aos desastres e aos processos de recuperação. A resposta ao terramoto de L’Aquila (no centro de Itália de 6 de Abril de 2009 é analisada enquanto ilustração dos processos de metamorfose cultural e interpretação simbólica dos desastres. Essa resposta foi influenciada pelas características culturais, tanto as modernas como as herdadas, que podem ser identificadas e analisadas de modo a explicar as reacções públicas ao acontecimento. Propõe‑se um novo modelo em que a cultura e a história se combinam com os perigos físicos para influenciar a vulnerabilidade.

  16. Adaptação do modelo de franchising, no setor óptico, em tempo de crise: o caso opticalia

    OpenAIRE

    Fernandes, Filipa

    2016-01-01

    Mestrado em Controlo de Gestão e dos Negócios Face à atual conjuntura económica e a outros elementos cruciais, como a concorrência, só uma minoria das empresas portuguesas consegue uma rede de grande dimensão. Neste contexto, a Opticalia é um caso paradigmático da vontade de empreender e progredir, equaciona abrir horizontes e desenvolver novas parcerias, com um modelo pioneiro e inovador para cativar óticas estagnadas e para criação de novos negócios. Este modelo veio agitar um mercado ma...

  17. Hot metal temperature prediction and simulation by fuzzy logic in a blast furnace

    International Nuclear Information System (INIS)

    Romero, M. A.; Jimenez, J.; Mochon, J.; Formoso, A.; Bueno, F.; Menendez, J. L.

    2000-01-01

    This work describes the development and further validation of a model devoted to blast furnace hot metal temperature forecast, based on Fuzzy logic principles. The model employs as input variables, the control variables of an actual blast furnace: Blast volume, moisture, coal injection, oxygen addition, etc. and it yields as a result the hot metal temperature with a forecast horizon of forty minutes. As far as the variables used to develop the model have been obtained from data supplied by an actual blast furnaces sensors, it is necessary to properly analyse and handle such data. Especial attention was paid to data temporal correlation, fitting by interpolation the different sampling rates. In the training stage of the model the ANFIS (Adaptive Neuro-Fuzzy Inference System) and the Subtractive Clustering algorithms have been used. (Author) 9 refs

  18. Ciclo de vida organizacional pautado no modelo de Lester, Parnell e Carraher (2003 e na lógica fuzzy: classificação de empresas de um segmento industrial de Santa Catarina

    Directory of Open Access Journals (Sweden)

    Ilse Maria Beuren

    2012-06-01

    Full Text Available Neste estudo, objetiva-se identificar os estágios do ciclo de vida organizacional pautados no modelo de Lester, Parnell e Carraher (2003 das empresas do segmento industrial de máquinas, aparelhos e materiais elétricos do estado de Santa Catarina. Pesquisa descritiva, com abordagem quantitativa, foi realizada por meio de levantamento com aplicação de questionário aos gestores das empresas. A população constituiu-se das 264 empresas desse segmento econômico, listadas na Secretaria da Fazenda do Estado de Santa Catarina, e a amostra não aleatória das 40 empresas que responderam a pesquisa. As variáveis de identificação dos estágios de ciclo de vida utilizadas no questionário foram extraídas de Lester, Parnell e Carraher (2003. Os dados da pesquisa foram submetidos à técnica estatística denominada lógica fuzzy. Os resultados da pesquisa demonstraram que 57,5% das empresas foram classificadas no estágio do nascimento, 15% do, 7,5% da Maturidade, 10% do rejuvenescimento e 10% do declínio. Concluiu-se que determinados estágios do ciclo de vida organizacional estão próximos uns dos outros e que não se pode perceber claramente uma progressão determinista nas fases do ciclo de vida, como uma sequência única, definitiva e irreversível, no sentido tradicional biológico.

  19. Modelo de simulação para o Sistema de Corte, Carregamento e Transporte de cana-de-açúcar: um estudo de caso no Estado do Rio de Janeiro

    Directory of Open Access Journals (Sweden)

    João José de Assis Rangel

    2010-05-01

    Full Text Available Neste trabalho foi desenvolvido um modelo de simulação para o Sistema de Corte, Carregamento e Transporte da cana-de-açúcar (conhecido como CCT das frentes de corte até uma usina localizada no norte do Estado do Rio de Janeiro. O objetivo do modelo foi analisar parâmetros do sistema, a partir do carregamento de cana-de-açúcar, sendo estes influenciados por operações de descarregamento na usina. Foi avaliado, então, o impacto do tempo de descarregamento da cana na usina e as suas implicações no sistema de corte, carregamento e transporte, considerando diferentes cenários.

  20. A Desigualdade Pode Afetar a Eficiência do Sistema Financeiro? Um Modelo de Equilíbrio em Dois Períodos com Fricções na Intermediação Financeira.

    Directory of Open Access Journals (Sweden)

    Wenersamy Ramos de Alcântara

    2015-03-01

    Full Text Available Este trabalho apresenta um modelo de equilíbrio que mostra um canal através do qual a desigualdade na distribuição de riqueza pode afetar o desenvolvimento do sistema financeiro. Na presença de fricções, nominalmente: um limite à proporção de dívida nos projetos financiados pelos bancos e um tamanho mínimo dos projetos disponíveis na economia, mostramos que a desigualdade pode afetar o volume intermediado pelo sistema financeiro e consequentemente, considerando ganhos de escala, sua eficiência. Simulações com diversas parametrizações revelam uma relação não linear e não monotônica entre desigualdade e eficiência do sistema financeiro, medida pelo spread entre captação e concessão de empréstimos.

  1. CULTURA ORGANIZACIONAL E GESTÃO EM EMPRESAS DE ECONOMIA CRIATIVA: UM ESTUDO COMPARATIVO COM MODELOS DE GESTÃO CONTEMPORÂNEOS

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    Ernani Cesar de Freitas

    2014-12-01

    Full Text Available Este artigo apresenta uma pesquisa sobre a cultura organizacional e a gestão em empresas de economia criativa. O tema é emergente e teve como motivação um estudo divulgado pela Federação das Indústrias do Rio de Janeiro – Sistema Firjan – divulgado em 2008. O presente trabalho visa mostrar características da cultura organizacional que diferenciam as empresas de economia criativa de seus concorrentes, no que se refere aos modelos de gestão. Para tanto, busca-se compreender os elementos básicos da cultura organizacional em empresas de economia criativa, delimitar aspectos inerentes da cultura organizacional dessas empresas, com a finalidade de estabelecer semelhanças e diferenças em relação a seus concorrentes, não classificados nessa configuração criativa. Na pesquisa,verificam-se processos específicos de inovação e criação que podem refletir nos modelos de gestão, além de identificar o perfil dos gestores que atuam no segmento de economia criativa em contraste aos profissionais das demais empresas. Para o constructo metodológico, realizou-se uma pesquisa exploratória mediante estudos de caso múltiplos com abordagem qualitativa. O corpus de pesquisa constitui-se de entrevistas não estruturadas, com quatro gestores da região do Vale do Sinos/RS. A questão norteadora buscou compreender, efetivamente, elementos potenciais, tais como a criatividade e a inovação e como estas poderiam influenciar positivamente a tomada de gestão nas empresas de economia criativa. Os resultados do estudo apontam que um dos fatores que mais se diferenciam em uma empresa de economia criativa, em relação às empresas tradicionais, é a capacidade de desenvolver a propriedade intelectual, que tem na criação o seu próprio atributo e se constitui no principal ativo da empresa. Outros fatores, tais como confiabilidade, retenção de talentos e flexibilidade de horários também foram pontuados. Em relação às vantagens e desvantagens

  2. Podemos prever a taxa de cambio brasileira? Evidência empírica utilizando inteligência computacional e modelos econométricos Can we forecast Brazilian exchange rates? Empirical evidences using computational intelligence and econometric models

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    Leandro dos Santos Coelho

    2008-12-01

    Full Text Available As abordagens de inteligência computacional, tais como sistemas nebulosos e redes neurais artificiais, têm-se gradualmente estabelecido como ferramentas robustas para a tarefa de aproximação de sistemas não-lineares complexos e previsão de séries temporais. Em aplicações envolvendo a área de Finanças, evidências empíricas anteriores indicam que modelos de inteligência computacional são mais precisos, dada sua maior capacidade em capturar não-linearidades e outros fatos estilizados presentes em séries financeiras. Nesse sentido, este artigo investiga a hipótese de que os modelos matemáticos de redes neurais perceptron multicamadas, redes neurais função de base radial e o sistema nebuloso Takagi-Sugeno (TAKAGI; SUGENO, 1985 são capazes de fornecer uma previsão fora-da-amostra mais acurada que os modelos auto-regressivos de médias móveis (ARMA e auto-regressivo de médias móveis supondo heterocedasticidade condicional auto-regressiva (ARMA-GARCH. O desempenho de previsão um-passo-à-frente dos modelos foi comparado utilizando-se séries de retorno da taxa de câmbio real/dólar (R$/US$ com freqüências de 15 minutos, 60 minutos, 120 minutos, diária e semanal. Resultados indicam que o desempenho dos modelos está diretamente relacionado à freqüência observada das séries. Além disso, os modelos de redes neurais obtiveram um desempenho superior em relação aos demais modelos considerados. A avaliação da estratégia de negociação estabelecida com base nas previsões geradas pelos modelos indicou que estratégias baseadas em modelos de redes neurais forneceram retornos superiores em relação àquelas baseadas em modelos ARMA e ARMA-GARCH e também em relação à estratégia buy-and-hold.Computational intelligence approaches, such as artificial neural networks and fuzzy systems, have become popular tools in approximating complicated nonlinear systems and time series forecasting. In Finance applications, there is

  3. Uma extensão ao modelo Schumpeteriano de Crescimento Endógeno

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    Marco Flávio da Cunha Resende

    2006-03-01

    Full Text Available O modelo Schumpeteriano de Crescimento Endógeno considera o progresso técnico determinante fundamental do crescimento econômico. Porém, ainda não conseguiu explicar como as inovações tecnológicas são geradas. Nesse modelo, elas ocorrem aleatoriamente. Todavia, os fatos sugerem uma explicação que apresenta um componente determinístico (tendência e outro componente aleatório para o surgimento das inovações. Portanto, foi desenvolvida neste trabalho uma extensão ao modelo schumpeteriano que visa incorporar um componente determinístico das inovações, além do componente aleatório. A partir desta modificação do modelo e da simulação da trajetória da renda per capita de cinco países entre 1800 e 2000 constatou-se que esta extensão ao modelo schumpeteriano pode explicar diversos fatos da realidade que o modelo básico não explicava.In the Schumpeterian endogenous growth model, random innovations (technical progress are the main element that explains economic growth. Empirical analyses suggest there are two variables that explain the introduction of innovations: a randomly variable and a deterministic trend. In this paper we add a deterministic variable to the basic Schumpeterian growth model. The introduction of a determinist variable improves the basic model. The new model reproduces several styled facts, which are shown in simulations.

  4. Prakiraan Beban Listrik Jangka Pendek Kota Banda Aceh Berbasis Logika Fuzzy

    OpenAIRE

    Syukriyadin,; Syahputra, Rio

    2012-01-01

    One of the technical aspects that support the optimal operation planning of a power plant when viewed in terms of system reliability and economic is about short-term load forecasting. The objective of this research is to forcasting hourly short-term electric load peak (17:30 to 22:30 GMT) at loading area of Transmission Distribution Banda Aceh Unit of PT. PLN P3B Aceh 150-20 kV by using Adaptive Neuro Fuzzy Inference System (ANFIS) method. The toolbox used to predict short-term electric load ...

  5. Predicción y simulación, mediante lógica difusa, de la temperatura de salida del arrabio en un horno alto

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    Romero, Miguel Angel

    2000-02-01

    Full Text Available This work describes the development and further validation of a model devoted to blast furnace hot metal temperature forecast, based on Fuzzy logic principles. The model employs as input variables, the control variables of an actual blast furnace: Blast volume, moisture, coal injection, oxygen addition, etc. and it yields as a result the hot metal temperature with a forecast horizon of forty minutes. As far as the variables used to develop the model have been obtained from data supplied by an actual blast furnace sensors, it is necessary to properly analyze and handle such data. Especial attention was paid to data temporal correlation, fitting by interpolation the different sampling rates. In the training stage of the model the ANFIS (Adaptive Neuro-Fuzzy Inference System and the Subtractive Clustering algorithms have been used.

    El presente artículo describe el desarrollo y posterior validación de un modelo para predecir la temperatura del arrabio de un horno alto, basado en lógica difusa. El modelo emplea, como variables de entrada, las variables de control propias del horno: caudal de viento, humedad, inyección de carbón, adición de oxígeno, etc. y obtiene, como resultado, el valor de la temperatura del arrabio producido por el horno, con un horizonte de predicción de 40 min. Las variables empleadas para el desarrollo del modelo se obtuvieron de los datos suministrados por lo sensores de un horno alto real. Fue preciso analizar y tratar adecuadamente dichos datos antes de introducirlos en el modelo. Se prestó especial atención a la correlación temporal de los datos adecuando mediante interpolación los distintos períodos de muestreo. El modelo se entrenó utilizando dos tipos de algoritmos: un sistema de inferencia neuro-difuso adaptativo (ANFIS y el Agrupamiento Sustractivo.

  6. The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data

    Science.gov (United States)

    Husein, A. M.; Harahap, M.; Aisyah, S.; Purba, W.; Muhazir, A.

    2018-03-01

    Medication planning aim to get types, amount of medicine according to needs, and avoid the emptiness medicine based on patterns of disease. In making the medicine planning is still rely on ability and leadership experience, this is due to take a long time, skill, difficult to obtain a definite disease data, need a good record keeping and reporting, and the dependence of the budget resulted in planning is not going well, and lead to frequent lack and excess of medicines. In this research, we propose Adaptive Neuro Fuzzy Inference System (ANFIS) method to predict medication needs in 2016 and 2017 based on medical data in 2015 and 2016 from two source of hospital. The framework of analysis using two approaches. The first phase is implementing ANFIS to a data source, while the second approach we keep using ANFIS, but after the process of clustering from K-Means algorithm, both approaches are calculated values of Root Mean Square Error (RMSE) for training and testing. From the testing result, the proposed method with better prediction rates based on the evaluation analysis of quantitative and qualitative compared with existing systems, however the implementation of K-Means Algorithm against ANFIS have an effect on the timing of the training process and provide a classification accuracy significantly better without clustering.

  7. INDUÇÃO DE TENDINOPATIA DEAQUILES EM MODELO ANIMAL: REVISÃO BIBLIOGRÁFICA

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    Leticia Boldo de Sousa

    2017-02-01

    Full Text Available Tendinopatia é uma lesão no tendão que varia de dor e/ou inflamação à condição crônica. Afeta pessoas de meia-idade e atinge 14% dos atletas de elite. O tratamento é com medicação orientada pelo especialista, além de atividades físicas que fortalecem a região afetada e com muita fisioterapia, enquanto os sucessos cirúrgicos são baixos e requerem um tratamento de longa duração. Esta revisão bibliográfica tem como objetivo mostrar os diferentes tipos de protocolos usando  modelo animal para induzir tendinopatia de aquiles. Foram utilizados artigos dos últimos 5 anos, publicados no Lillacs, Pubmed e Scielo, descartando os que não usaram modelo mecânico de indução. Não há modelos ideais que se aproximam da realidade humana e cada um tem suas vantagens e desvantagens, sendo necessários mais estudos aprofundados de tendinopatia no aquiles em modelo animal.   Palavras-chave: tendão de Aquiles, tendinopatia, modelo animal, corrida. Área do Conhecimento: Ciências da Saúde

  8. Proposta de um modelo referencial de gestão de indicadores de qualidade na instituição universitária Propuesta de un modelo de referencia para la gestión de indicadores de calidad en la universidad A reference model proposal for management of the quality indicators at universities

    Directory of Open Access Journals (Sweden)

    Nilsa Maria Guarda Canterle

    2008-09-01

    Full Text Available O presente artigo procura desenvolver um modelo referencial de gestão que capte os determinantes da qualidade na universidade, uma vez que esta, cada vez mais tem sido desafiada, a demonstrar qualidade no seu desempenho. Do ponto de vista do problema, a pesquisa se caracteriza como predominantemente qualitativa e do ponto de vista dos seus objetivos como exploratória e descritiva. Quanto ao ponto de vista da linha de raciocínio, o procedimento foi a construção de um modelo, sendo que as técnicas utilizadas, enquanto conjunto de procedimentos práticos da pesquisa, foram a pesquisa bibliográfica e documental. Os dados foram obtidos a partir de fontes secundárias, estando a pesquisa centrada no estudo de doze modelos de qualidade na universidade, relatados na literatura, nos quais buscaram-se as semelhanças entre um conjunto de universidades e, ao mesmo tempo, tentou-se entender suas singularidades. O processo de coleta e análise dos dados permitiu identificar a ocorrência de eventos associados à qualidade nas instituições pesquisadas, bem como obter uma descrição dos indicadores essenciais ao desenho de um modelo de gestão universitária mais próximo da realidade. O modelo desenvolvido é orientado pelos elementos característicos da qualidade e, pactuado no diálogo, potencializa a avaliação como elo de feedback para a garantia e melhoria da qualidade. De formato simples, o modelo propicia uma forma eficaz de agir sobre a qualidade.Este artículo tiene por objeto desarrollar un modelo de referencia de gestión que recoge los factores determinantes de la calidad en las universidades, ya que, cada vez más ha sido desafiada a demostrar calidad en su desempeño. Desde el punto de vista del problema, la búsqueda se caracteriza por ser predominantemente cualitativa y en términos de sus objetivos como exploratoria y descriptiva. A partir de una línea de razonamiento, el procedimiento fue la construcción de un modelo, y las t

  9. Um modelo de otimização para alíquotas do IPTU socialmente mais justas Un modelo de optimización de las tasas de impuestos a la propiedad socialmente más justo An optimization model for rates of socially fairer property tax

    Directory of Open Access Journals (Sweden)

    José Delfino Sá

    2013-02-01

    Full Text Available Este artigo apresenta um modelo de otimização matemática não linear que determina novas alíquotas para o Imposto sobre a Propriedade Predial e Territorial Urbana (IPTU incidente sobre os apartamentos residenciais no município do Salvador (BA. São considerados a progressividade das alíquotas, os valores venais dos imóveis, as rendas médias dos contribuintes e as usuais metodologias de cálculo do valor do imposto. Os resultados obtidos na aplicação desse modelo demonstram ser possível tratar de forma objetiva e socialmente mais justa a definição das alíquotas de IPTU para todos os tipos de imóveis de um município.En este trabajo se presenta un modelo de optimización matemática no lineal que determinan las nuevas tarifas del Impuesto Inmobiliario Urbano - Impuesto sobre la propiedad aplicado en apartamentos residenciales en la ciudad de Salvador (BA. Ellos se consideran la progresividad de los tipos impositivos, el mercado de valores de las propiedades, los ingresos medios de los contribuyentes y de los métodos habituales de cálculo de la cuantía del impuesto. Los resultados demuestran que la aplicación de este modelo se puede tratar de manera objetiva y socialmente más justo fijar los tipos de impuestos a la propiedad para todos los tipos de propiedades en un municipio.This article presents a nonlinear mathematical optimization model that determines new rates for the Urban Property Tax (IPTU on the residential apartments in the city of Salvador (BA. It considers the progressive increase in rates, the market values of properties, the taxpayers' average incomes, and the usual methodologies of the tax system. The results obtained in applying this model demonstrate that is possible to set tax rates in an objective and equitable manner in all kinds of urban property.

  10. Avaliação de empresas: um estudo comparativo entre o modelo de capitalização dos lucros e o modelo dos múltiplos do fluxo de caixa

    Directory of Open Access Journals (Sweden)

    Sady Mazzioni

    2005-07-01

    Full Text Available O debate acerca dos processos de avaliação de empresas está em plena efervescência, instigado pela globalização dos mercados e pelo dinamismo do mundo dos negócios. Os fatores que contribuem de modo mais consistente para isso compreendem desde o interesse nos procedimentos de fusão, incorporação, cisão e alienação até a necessidade da informação para algum objetivo qualquer. O objetivo principal deste estudo é apresentar um enfoque no modelo de capitalização dos lucros comparativamente ao modelo dos múltiplos do fluxo de caixa, evidenciando as peculiaridades de cada proposta. O estudo de caso foi desenvolvido em uma indústria que atua na produção e comercialização de complementos alimentares. Constata-se que geralmente o processo de avaliação de empresas reveste-se de subjetividade ao se determinar variáveis, como período de projeção, valor da perpetuidade, taxa de desconto, custo de capital e custo de oportunidade da perspectiva pessoal do avaliador e tal foi constatado na empresa estudada. Verificou-se que os modelos de avaliação são complementares uns dos outro se não excludentes. Conclui-se que o processo de avaliação deve ser composto por um conjunto de métodos, responsáveis por uma avaliação mais completa e com menor risco de imperfeições na determinação final do valor de determinada empresa. The debate about the analysis processes of companies is booming, stimulated by themarket globalization and by the dynamism of the world of business. The factors thatcontribute in a most consistent way to this, include the interest in the fusion procedures,incorporation, division and alienation, or the need of information for any objective. Theprincipal objective of this study is to present a model focus in the profits capitalization,comparatively to the multiples of the cash flow, confirming the peculiarities of eachpropose. The study of the case was developed in an industry of the food sector that

  11. Metodologia ativa na engenharia: verificação da abp em uma disciplina de engenharia de produção e um modelo passo a passo

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    Thales Martins Ponciano

    2017-06-01

    Full Text Available Este artigo verifica a aplicação da metodologia Aprendizagem Baseada em Problema (ABP na disciplina de Sistemas de Desenvolvimento de Produto do curso de Engenharia de Produção de uma universidade pública brasileira. Por meio da caracterização da metodologia foi possível observar a aplicabilidade dessa ferramenta em um curso de Engenharia que envolve diversas áreas do conhecimento e concilia atividades teóricas e práticas. De modo geral, as metodologias ativas, como a ABP, podem estimular os alunos a desenvolverem habilidades, como organização, liderança e pensamento crítico, que ultrapassam o conteúdo programático das disciplinas. Sendo assim, foi proposto um modelo passo a passo para a aplicação da ABP.

  12. MODELO MATEMÁTICO DE UN REFRIGERADOR POR ADSORCIÓN DE METANOL EN CARBÓN ACTIVO MODELO MATEMÁTICO DE UM FRIGORÍFICO PARA A ADSORÇÃO DE METANOL EM CARVÃO MATHEMATICAL MODEL OF AN ADSORPTION REFRIGERATOR OF METHANOL IN ACTIVE COAL

    Directory of Open Access Journals (Sweden)

    FARID B CORTÉS

    2010-12-01

    Full Text Available Un modelo matemático no estacionario fue utilizado para describir el proceso de adsorción y desorción de metanol en carbón activado funcional para el entendimiento de un refrigerador alternativo. Este modelo de carácter fenomenológico conformado por los balances de materia, energía y cantidad de movimiento en coordenadas cilíndricas se discretizó en el espacio por el método de volúmenes finitos, utilizando la aproximación de un esquema de diferencia central y upwind de primer orden para los términos difusivos y convectivos, respectivamente, bajo una plataforma FORTRAN 90. Los resultados obtenidos de la simulación fueron validados satisfactoriamente con información experimental obtenida de datos de pruebas de campo y con datos reportados en la literatura, presentando errores inferiores al 1.6% para la etapa de adsorción - evaporación de un ciclo de refrigeración. El modelo permite obtener los perfiles de temperatura, presión, densidad y velocidad del gas en dirección radial como también la temperatura del sólido y la cantidad adsorbida (o desorbida de metanol en el lecho de carbón activado, durante las etapas de adsorción/evaporación y desorción/condensación.Um modelo não-estacionário matemático foi usado para descrever a adsorção e dessorção de metanol no entendimento de carvão ativado funcional deum cooleralternativo. Este modelo fenomenológico consiste na energia material saldos emomentum em coordenadas cilindricas é discretizado no espaço pelo método de volumes finitos, utilizando a abordagem de um esquema de diferença central e upwindde primeira ordem para os termos difusivos e convecção, respectivamente,