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
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.
Ghanbari, M.; Najafi, G.; Ghobadian, B.; Mamat, R.; Noor, M. M.; Moosavian, A.
2015-12-01
This paper studies the use of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For ANFIS modelling, Gaussian curve membership function (gaussmf) and 200 training epochs (iteration) were found to be optimum choices for training process. The results demonstrate that ANFIS is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nanostructure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve combustion of the fuel and reduce the exhaust emissions significantly.
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)
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.
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
A new learning algorithm for a fully connected neuro-fuzzy inference system.
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.
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
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.
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.
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)
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
Adaptive neuro-fuzzy inference system for forecasting rubber milk production
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).
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)
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...
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.
Prediction of Scour Depth around Bridge Piers using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
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.
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.
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.
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....
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.
Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.
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
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.
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
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...
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.
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)
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.
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)
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
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
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.
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.
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
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.
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
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.
Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping
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.
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.
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.
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)
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.
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.
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)
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.
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)
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
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.
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.
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.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
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.
Sezer, Ebru; Pradhan, Biswajeet; Gokceoglu, Candan
2010-05-01
Landslides are one of the recurrent natural hazard problems throughout most of Malaysia. Recently, the Klang Valley area of Selangor state has faced numerous landslide and mudflow events and much damage occurred in these areas. However, only little effort has been made to assess or predict these events which resulted in serious damages. Through scientific analyses of these landslides, one can assess and predict landslide-susceptible areas and even the events as such, and thus reduce landslide damages through proper preparation and/or mitigation. For this reason , the purpose of the present paper is to produce landslide susceptibility maps of a part of the Klang Valley areas in Malaysia by employing the results of the adaptive neuro-fuzzy inference system (ANFIS) analyses. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map vegetation index. Maps of topography, lineaments and NDVI were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using an ANFIS to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient
Razavi Termeh, Seyed Vahid; Kornejady, Aiding; Pourghasemi, Hamid Reza; Keesstra, Saskia
2018-02-15
Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the
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.
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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.
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.
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.
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...
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
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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.
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.
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.
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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.
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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.
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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.
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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.
Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system
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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.
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.
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.
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.
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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.
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.
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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.
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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.
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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)
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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)
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.
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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.
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.
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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.
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.
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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)
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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.
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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.
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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.
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Erhankana Ardiana Putra
2017-01-01
Full Text Available Pada sistem kelistrikan terutama pada sistem proteksi kelistrikan dewasa ini sangat dibutuhkan sistem yang handal, sehingga perkembangan pada sistem proteksi sudah semakin maju dengan adanya penggunaan rele digital. Rele digital digunakan dengan mempertimbangkan kecepatan, keakuratan dan serta flexible dalam sistem koordinasi. Flexibilitas ini dimaksudkan bahwa rele digital dapat digunakan menjadi rele arus lebih (overcurrent relay sesuai pembahasan tugas akhir ini dan dapat disetting menurut keinginan user sesuai karakteristik kurva OCR konvensional/standart (normal inverse, very inverse, long time inverse, extreme inverse yang akan digunakan dalam koordinasi. Jenis kurva pada rele digital juga dapat disetting diluar rumus kurva konvensional/standart yang seperti sudah disebutkan sebelumnya, kurva diluar rumusan standart disebut kurva rele non-standart. Kurva rele non-standart digunakan untuk memudahkan pengguna untuk menentukan waktu trip berdasarkan arus yang diinginkan dan sebagai solusi jika pada koordinasi proteksi mengalami kendala dalam koordinasi kurva rele. Pada tugas akhir ini akan dibahas bagaimana membuat atau memodelkan kurva karakteristik inverse overcurrent rele non-standart dengan menggunakan metode (Adaptive Neuro Fuzzy Inference System atau biasa disebut metode pembelajaran ANFIS. Kurva non-standart didapatkan dengan pengambilan titik-titik data baru berupa arus dan waktu trip sesuai keinginan user. Data baru tersebut akan digabungkan dengan data lama sehingga menghasilkan data non-standart yang nantinya akan dilakukan pembelajaran dengan metode ANFIS untuk mendapatkan desain kurva non-standart. Setelah didapatkan desain kurva non-standart akan dilakukan pengujian keakuratan dengan mengganti nilai MF (membership function didapatkan hasil rata-rata error terkecil 2,56% (MF=10 dan epoch=100. Pengujian selanjutnya dengan mengubah nilai epoch didapatkan nilai keakuratan dengan error terkecil pada epoch = 500. Simulasi pada
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.
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
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...
Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid
2016-08-01
This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS
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.
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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.
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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.
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.
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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 neuro –fuzzy 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.
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.
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.
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
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)
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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.
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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.
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.
Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo
2017-11-01
The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.
International Nuclear Information System (INIS)
Sumithira, T. R.; Nirmal, Kumar A.
2012-01-01
Enormous potential of solar energy as a clean and pollution free source enrich the global power generation. India, being a tropical country, has high solar radiation and it lies to the north of equator between 8 degree 4' and 37 degree 6' North latitude and 68 degree 7' , and 97 degree 5' East longitude. In south india, Tamilnadu is located in the extreme south east with an average temperature of grater than 27.5 degree (> 81.5 F). In this study, an adaptive neuro-fuzzy inference system (ANFIS) based modelling approach to predict the monthly global solar radiation (MGSR) in Tamilnadu is presented using the real meteorological solar radiation data from the 31 districts of Tamilnadu with different latitude and longitude. The purpose of the study is to compare the accuracy of ANFIS and other soft computing models as found in literature to assess the solar radiation. The performance of the proposed model was tested and compared with other earth region in a case study. The statistical performance parameters such as root mean square error (RMSE), mean bias error (MBE), and coefficient of determination (R2) are presented and compared to validate the performance. The comparative test results prove the ANFIS based prediction are better than other models and furthermore proves its prediction capability for any geographical area with changing meteorological conditions. (author)
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
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.
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.
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.
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.
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)
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.
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.
Asnaashari, Maryam; Farhoosh, Reza; Farahmandfar, Reza
2016-10-01
As a result of concerns regarding possible health hazards of synthetic antioxidants, gallic acid and methyl gallate may be introduced as natural antioxidants to improve oxidative stability of marine oil. Since conventional modelling could not predict the oxidative parameters precisely, artificial neural network (ANN) and neuro-fuzzy inference system (ANFIS) modelling with three inputs, including type of antioxidant (gallic acid and methyl gallate), temperature (35, 45 and 55 °C) and concentration (0, 200, 400, 800 and 1600 mg L(-1) ) and four outputs containing induction period (IP), slope of initial stage of oxidation curve (k1 ) and slope of propagation stage of oxidation curve (k2 ) and peroxide value at the IP (PVIP ) were performed to predict the oxidation parameters of Kilka oil triacylglycerols and were compared to multiple linear regression (MLR). The results showed ANFIS was the best model with high coefficient of determination (R(2) = 0.99, 0.99, 0.92 and 0.77 for IP, k1 , k2 and PVIP , respectively). So, the RMSE and MAE values for IP were 7.49 and 4.92 in ANFIS model. However, they were to be 15.95 and 10.88 and 34.14 and 3.60 for the best MLP structure and MLR, respectively. So, MLR showed the minimum accuracy among the constructed models. Sensitivity analysis based on the ANFIS model suggested a high sensitivity of oxidation parameters, particularly the induction period on concentrations of gallic acid and methyl gallate due to their high antioxidant activity to retard oil oxidation and enhanced Kilka oil shelf life. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.
Ghaedi, M.; Hosaininia, R.; Ghaedi, A. M.; Vafaei, A.; Taghizadeh, F.
2014-10-01
In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope (SEM), Brunauer-Emmett-Teller (BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55 m2/g) and low pore size (influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02 g adsorbent mass, 10 mg L-1 initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30 min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R2) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.
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.
He, Zhibin; Wen, Xiaohu; Liu, Hu; Du, Jun
2014-02-01
Data driven models are very useful for river flow forecasting when the underlying physical relationships are not fully understand, but it is not clear whether these data driven models still have a good performance in the small river basin of semiarid mountain regions where have complicated topography. In this study, the potential of three different data driven methods, artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for forecasting river flow in the semiarid mountain region, northwestern China. The models analyzed different combinations of antecedent river flow values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANN, ANFIS and SVM models in training and validation sets are compared with the observed data. The model which consists of three antecedent values of flow has been selected as the best fit model for river flow forecasting. To get more accurate evaluation of the results of ANN, ANFIS and SVM models, the four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and mean absolute relative error (MARE), were employed to evaluate the performances of various models developed. The results indicate that the performance obtained by ANN, ANFIS and SVM in terms of different evaluation criteria during the training and validation period does not vary substantially; the performance of the ANN, ANFIS and SVM models in river flow forecasting was satisfactory. A detailed comparison of the overall performance indicated that the SVM model performed better than ANN and ANFIS in river flow forecasting for the validation data sets. The results also suggest that ANN, ANFIS and SVM method can be successfully applied to establish river flow with complicated topography forecasting models in the semiarid mountain regions.
Energy Technology Data Exchange (ETDEWEB)
Entchev, Evgueniy; Yang, Libing [Integrated Energy Systems Laboratory, CANMET Energy Technology Centre, 1 Haanel Dr., Ottawa, Ontario (Canada)
2007-06-30
This study applies adaptive neuro-fuzzy inference system (ANFIS) techniques and artificial neural network (ANN) to predict solid oxide fuel cell (SOFC) performance while supplying both heat and power to a residence. A microgeneration 5 kW{sub el} SOFC system was installed at the Canadian Centre for Housing Technology (CCHT), integrated with existing mechanical systems and connected in parallel to the grid. SOFC performance data were collected during the winter heating season and used for training of both ANN and ANFIS models. The ANN model was built on back propagation algorithm as for ANFIS model a combination of least squares method and back propagation gradient decent method were developed and applied. Both models were trained with experimental data and used to predict selective SOFC performance parameters such as fuel cell stack current, stack voltage, etc. The study revealed that both ANN and ANFIS models' predictions agreed well with variety of experimental data sets representing steady-state, start-up and shut-down operations of the SOFC system. The initial data set was subjected to detailed sensitivity analysis and statistically insignificant parameters were excluded from the training set. As a result, significant reduction of computational time was achieved without affecting models' accuracy. The study showed that adaptive models can be applied with confidence during the design process and for performance optimization of existing and newly developed solid oxide fuel cell systems. It demonstrated that by using ANN and ANFIS techniques SOFC microgeneration system's performance could be modelled with minimum time demand and with a high degree of accuracy. (author)
Allawi, Mohammed Falah; Jaafar, Othman; Mohamad Hamzah, Firdaus; Mohd, Nuruol Syuhadaa; Deo, Ravinesh C.; El-Shafie, Ahmed
2017-10-01
Existing forecast models applied for reservoir inflow forecasting encounter several drawbacks, due to the difficulty of the underlying mathematical procedures being to cope with and to mimic the naturalization and stochasticity of the inflow data patterns. In this study, appropriate adjustments to the conventional coactive neuro-fuzzy inference system (CANFIS) method are proposed to improve the mathematical procedure, thus enabling a better detection of the high nonlinearity patterns found in the reservoir inflow training data. This modification includes the updating of the back propagation algorithm, leading to a consequent update of the membership rules and the induction of the centre-weighted set rather than the global weighted set used in feature extraction. The modification also aids in constructing an integrated model that is able to not only detect the nonlinearity in the training data but also the wide range of features within the training data records used to simulate the forecasting model. To demonstrate the model's efficacy, the proposed CANFIS method has been applied to forecast monthly inflow data at Aswan High Dam (AHD), located in southern Egypt. Comparative analyses of the forecasting skill of the modified CANFIS and the conventional ANFIS model are carried out with statistical score indicators to assess the reliability of the developed method. The statistical metrics support the better performance of the developed CANFIS model, which significantly outperforms the ANFIS model to attain a low relative error value (23%), mean absolute error (1.4 BCM month-1), root mean square error (1.14 BCM month-1), and a relative large coefficient of determination (0.94). The present study ascertains the better utility of the modified CANFIS model in respect to the traditional ANFIS model applied in reservoir inflow forecasting for a semi-arid region.
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.
Directory of Open Access Journals (Sweden)
mohsen Mirzayi
2016-03-01
Full Text Available Landscape indices can be used as an approach for predicting water quality changes to monitor non-point source pollution. In the present study, the data collected over the period from 2012 to 2013 from 81 water quality stations along the rivers flowing in Mazandaran Province were analyzed. Upstream boundries were drawn and landscape metrics were extracted for each of the sub-watersheds at class and landscape levels. Principal component analysis was used to single out the relevant water quality parameters and forward linear regression was employed to determine the optimal metrics for the description of each parameter. The first five components were able to describe 96.61% of the variation in water quality in Mazandaran Province. Adaptive Neuro-fuzzy Inference System (ANFIS and multiple linear regression were used to model the relationship between landscape metrics and water quality parameters. The results indicate that multiple regression was able to predict SAR, TDS, pH, NO3‒, and PO43‒ in the test step, with R2 values equal to 0.81, 0.56, 0.73, 0.44. and 0.63, respectively. The corresponding R2 value of ANFIS in the test step were 0.82, 0.79, 0.82, 0.31, and 0.36, respectively. Clearly, ANFIS exhibited a better performance in each case than did the linear regression model. This indicates a nonlinear relationship between the water quality parameters and landscape metrics. Since different land cover/uses have considerable impacts on both the outflow water quality and the available and dissolved pollutants in rivers, the method can be reasonably used for regional planning and environmental impact assessment in development projects in the region.
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.
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.
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.
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.
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 ...
International Nuclear Information System (INIS)
Ogaga Ighose, Benjamin; Adeleke, Ibrahim A.; Damos, Mueuji; Adeola Junaid, Hamidat; Ernest Okpalaeke, Kelechi; Betiku, Eriola
2017-01-01
Highlights: • Oil was extracted from Thevetia peruviana seeds and converted to FAME. • The FFA of the oil was first reduced to <1% by esterification process. • The conversion of the esterified oil to FAME was modeled using ANFIS and RSM. • The developed models by ANFIS and RSM for transesterification process had R"2 ≈ 1. • GA and RSM gave the maximum FAME yield of 99.8 wt.% and 98.8 wt.%, respectively. - Abstract: This work focused on the application of adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) as predictive tools for production of fatty acid methyl esters (FAME) from yellow oleander (Thevetia peruviana) seed oil. Two-step transesterification method was adopted, in the first step, the high free fatty acid (FFA) content of the oil was reduced to <1% by treating it with ferric sulfate in the presence of methanol. While in the second step, the pretreated oil was converted to FAME by reacting it with methanol using sodium methoxide as catalyst. To model the second step, central composite design was employed to study the effect of catalyst loading (1–2 wt.%), methanol/oil molar ratio (6:1–12:1) and time (20–60 min) on the T. peruviana methyl esters (TPME) yield. The reduction of FFA of the oil to 0.65 ± 0.05 wt.% was realized using ferric sulfate of 3 wt.%, methanol/FFA molar ratio of 9:1 and reaction time of 40 min. The model developed for the transesterification process by ANFIS (coefficient of determination, R"2 = 0.9999, standard error of prediction, SEP = 0.07 and mean absolute percentage deviation, MAPD = 0.05%) was significantly better than that of RSM (R"2 = 0.9670, SEP = 1.55 and MAPD = 0.84%) in terms of accuracy of the predicted TPME yield. For maximum TPME yield, the transesterification process input variables were optimized using genetic algorithm (GA) coupled with the ANFIS model and RSM optimization tool. TPME yield of 99.8 wt.% could be obtained with the combination of 0.79 w/v catalyst
Hayati, M.; Rashidi, A. M.; Rezaei, A.
2012-10-01
In this paper, the applicability of ANFIS as an accurate model for the prediction of the mass gain during high temperature oxidation using experimental data obtained for aluminized nanostructured (NS) nickel is presented. For developing the model, exposure time and temperature are taken as input and the mass gain as output. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the network. We have compared the proposed ANFIS model with experimental data. The predicted data are found to be in good agreement with the experimental data with mean relative error less than 1.1%. Therefore, we can use ANFIS model to predict the performances of thermal systems in engineering applications, such as modeling the mass gain for NS materials.
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)
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.
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.
Neuro-fuzzy controller to navigate an unmanned vehicle.
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).
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.
Landslide susceptibility mapping using a neuro-fuzzy
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
Kim, Chan Moon; Parnichkun, Manukid
2017-11-01
Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system ( k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.
Heidary, Saeed; Setayeshi, Saeed; Ghannadi-Maragheh, Mohammad
2014-09-01
The aim of this study is to compare the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network (ANN) to estimate the cross-talk contamination of 99 m Tc / 201 Tl image acquisition in the 201 Tl energy window (77 ± 15% keV). GATE (Geant4 Application in Emission and Tomography) is employed due to its ability to simulate multiple radioactive sources concurrently. Two kinds of phantoms, including two digital and one physical phantom, are used. In the real and the simulation studies, data acquisition is carried out using eight energy windows. The ANN and the ANFIS are prepared in MATLAB, and the GATE results are used as a training data set. Three indications are evaluated and compared. The ANFIS method yields better outcomes for two indications (Spearman's rank correlation coefficient and contrast) and the two phantom results in each category. The maximum image biasing, which is the third indication, is found to be 6% more than that for the ANN.
Savari, Maryam; Moghaddam, Amin Hedayati; Amiri, Ahmad; Shanbedi, Mehdi; Ayub, Mohamad Nizam Bin
2017-10-01
Herein, artificial neural network and adaptive neuro-fuzzy inference system are employed for modeling the effects of important parameters on heat transfer and fluid flow characteristics of a car radiator and followed by comparing with those of the experimental results for testing data. To this end, two novel nanofluids (water/ethylene glycol-based graphene and nitrogen-doped graphene nanofluids) were experimentally synthesized. Then, Nusselt number was modeled with respect to the variation of inlet temperature, Reynolds number, Prandtl number and concentration, which were defined as the input (design) variables. To reach reliable results, we divided these data into train and test sections to accomplish modeling. Artificial networks were instructed by a major part of experimental data. The other part of primary data which had been considered for testing the appropriateness of the models was entered into artificial network models. Finally, predictad results were compared to the experimental data to evaluate validity. Confronted with high-level of validity confirmed that the proposed modeling procedure by BPNN with one hidden layer and five neurons is efficient and it can be expanded for all water/ethylene glycol-based carbon nanostructures nanofluids. Finally, we expanded our data collection from model and could present a fundamental correlation for calculating Nusselt number of the water/ethylene glycol-based nanofluids including graphene or nitrogen-doped graphene.
Subashini, L.; Vasudevan, M.
2012-02-01
Type 316 LN stainless steel is the major structural material used in the construction of nuclear reactors. Activated flux tungsten inert gas (A-TIG) welding has been developed to increase the depth of penetration because the depth of penetration achievable in single-pass TIG welding is limited. Real-time monitoring and control of weld processes is gaining importance because of the requirement of remoter welding process technologies. Hence, it is essential to develop computational methodologies based on an adaptive neuro fuzzy inference system (ANFIS) or artificial neural network (ANN) for predicting and controlling the depth of penetration and weld bead width during A-TIG welding of type 316 LN stainless steel. In the current work, A-TIG welding experiments have been carried out on 6-mm-thick plates of 316 LN stainless steel by varying the welding current. During welding, infrared (IR) thermal images of the weld pool have been acquired in real time, and the features have been extracted from the IR thermal images of the weld pool. The welding current values, along with the extracted features such as length, width of the hot spot, thermal area determined from the Gaussian fit, and thermal bead width computed from the first derivative curve were used as inputs, whereas the measured depth of penetration and weld bead width were used as output of the respective models. Accurate ANFIS models have been developed for predicting the depth of penetration and the weld bead width during TIG welding of 6-mm-thick 316 LN stainless steel plates. A good correlation between the measured and predicted values of weld bead width and depth of penetration were observed in the developed models. The performance of the ANFIS models are compared with that of the ANN models.
Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.
2016-11-01
All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.
International Nuclear Information System (INIS)
Mostafaei, Mostafa; Javadikia, Hossein; Naderloo, Leila
2016-01-01
Biodiesel is as an alternative petro-diesel fuel produced from the renewable resources. The use of novel technologies such as ultrasound technology for biodiesel production intensifies the reaction and reduces the process cost. The present study is aimed to evaluate and compare the prediction and simulating efficiency of the response surface methodology (RSM) and adaptive Neuro-fuzzy inference system (ANFIS) approaches for modeling the transesterification yield achieved in ultrasonic reactor. The influence of independent variables (reactor diameter, liquid height and ultrasound intensity) on the conversion of fatty acid methyl esters (FAME) was investigated by Box-Behnken design of RSM and two ANFIS approaches (hybrid and back-propagation optimization methods). All models were compared statistically based on the training and validation data set by the coefficient of determination (R2), root mean squares error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean relative percent deviation (MRPD). The calculated R2 for RSM and two ANFIS models were 0.9669, 0.9812 and 0.9808, respectively. All models indicated good predictions, however, the ANFIS models were more precise compared to the RSM model, which proves that the ANFIS is a powerful tool for modeling and optimizing FAME production in ultrasound reactor. - Highlights: • The ultrasound assisted FAME conversion was modelled using RSM and ANFIS approaches. • The scatter diagrams indicate the models accurately predicted the reaction yield. • The ANFIS model (hybrid) has higher R"2 (0.9812) compared to the RSM model. • The predicted deviations and residual values are relatively small for ANFIS model. • ANFIS model was more accurate for predicting ultrasound assisted FAME conversion.
Directory of Open Access Journals (Sweden)
O Ghaderpour
2018-03-01
in impact assessment. The purpose of damage assessment is to combine a number of impact category indicators into a damage category (also called area of protection. To assess the damage in this study, IMPACT 2002+ V2.12 / IMPACT 2002+ method was used. ANFIS is a multilayer feed-forward network which is applying to map an input space to an output space using a combination of neural network learning algorithms and fuzzy reasoning. In order to enable a system to deal with cognitive uncertainties in a manner more like humans, neural networks have been engaged with fuzzy logic, creating a new terminology called ‘‘neuro-fuzzy method. An ANFIS is used to map input characteristics to input membership functions (MFs, input MF to a set of if-then rules, rules to a set of output characteristics, output characteristics to output MFs, and the output MFs to a single valued output or a decision associated with the output. The main restriction of the ANFIS model is related to the number of input variables. If ANFIS inputs exceed five, the computational time and rule numbers will increase, so ANFIS will not be able to model output with respect to inputs. In this study, the number of inputs were ten, including machinery, diesel fuel, nitrogen, phosphate, electricity, water for irrigation, labor, pesticides, Manure and seed and GWP was as the model output signal. To solve this problem and employ all input variables, we proposed clustering input parameters to four groups. Correspondingly, the proposed model was developed using seven ANFIS sub-networks. To obtain the best results several modifications were made in the structure of ANFIS networks, and some parameters were calculated to compare the results of different models. Making a comparison between different topologies the employment of some indicators was a pivotal to get a good vision of various the structures, such as the correlation coefficient (R, Mean Square Error (MSE and Root Mean Square Error (RMSE. In addition, for
A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets
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.
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 ...
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
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.
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.
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.
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.
Bazzazi, Abbas Aghajani; Esmaeili, Mohammad
2012-12-01
Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.
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.)
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.
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)
A neuro-fuzzy computing technique for modeling hydrological time series
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.
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.
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)
Design and implementation of an adaptive critic-based neuro-fuzzy controller on an unmanned bicycle
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...
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
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
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 ...
Developed adaptive neuro-fuzzy algorithm to control air conditioning ...
African Journals Online (AJOL)
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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, ...
System identification of smart structures using a wavelet neuro-fuzzy model
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.
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.
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)
Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia
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.
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.
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.
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
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.
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
Potential of neuro-fuzzy methodology to estimate noise level of wind turbines
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.
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.
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.
Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects
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
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
Neuro-fuzzy control of structures using acceleration feedback
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.
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.
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.
Application of adaptive neuro-fuzzy inference system technique in ...
African Journals Online (AJOL)
Journal of Agriculture, Science and Technology ... The recent explosion in information technology and wireless communications has created many opportunities ... This artificial Intelligence (AI) technique is used in determining the parameters ...
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
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.
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.
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.
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
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
RETRACTED: Adaptive neuro-fuzzy prediction of modulation transfer function of optical lens system
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.
Adaptive Neuro-Fuzzy Methodology for Noise Assessment of Wind Turbine
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
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.
Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology
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.
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.
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)
Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network
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.
Adaptive Neuro-Fuzzy Based Gain Controller for Erbium-Doped Fiber Amplifiers
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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.
Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.
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.
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.
Genetic-neuro-fuzzy system for grading depression
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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.
A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance
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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.
Spacecraft attitude control using neuro-fuzzy approximation of the optimal controllers
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.
Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration
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.
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)
Evaluation of Regression and Neuro_Fuzzy Models in Estimating Saturated Hydraulic Conductivity
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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.
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.
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.
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.
A TSK neuro-fuzzy approach for modeling highly dynamic systems
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
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
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
Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model.
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.
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.
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.
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)
Integration of Fault Detection and Isolation with Control Using Neuro-fuzzy Scheme
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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.
Innovative neuro-fuzzy system of smart transport infrastructure for road traffic safety
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.
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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.
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.
Textual and shape-based feature extraction and neuro-fuzzy classifier for nuclear track recognition
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.
Evaluating Loans Using a Combination of Data Envelopment and Neuro-Fuzzy Systems
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, ...
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)
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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.
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)
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
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.
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.
A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data
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
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.
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
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.
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.
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.
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.
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)
A genetic neuro-fuzzy logic for DNB protection
International Nuclear Information System (INIS)
Na, Man Gyun
1999-01-01
A neurofuzzy method is used to estimate the DNB protection limit using the measured average temperature and pressure of a reactor core. The neurofuzzy system parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the neurofuzzy inference system and a least-squares algorithm to solve the consequent parameters. Two neurofuzzy inference systems are used according to the pressure and temperature regions. The proposed method is applied to the Yonggwang 3 and 4 nuclear power plant and the proposed method has 5.84 percent larger thermal margin than the conventional Westinghouse ΟΤΔΤ trip logic. This simple algorithm can provide a good information for the nuclear power plant operation and diagnosis by estimating the DNB protection limit each time step
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
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
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)
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)
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
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.
Developed adaptive neuro-fuzzy algorithm to control air conditioning ...
African Journals Online (AJOL)
user
... conditioning system is highly appreciated and essential in most of our daily life. ... (Hossien and Karla, 2012) presented an overview work which provides an .... energy balance for SSSF and the mass flow balance for the water in the air are ..... of Automatic Control and Electrical Engineering at Siegen University, Germany.
Statistical inference for financial engineering
Taniguchi, Masanobu; Ogata, Hiroaki; Taniai, Hiroyuki
2014-01-01
This monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear processes, non-linear processes, long-memory processes, locally stationary processes etc. are introduced and their optimal estimation is considered as well. This book also includes several statistical approaches, e.g., discriminant analysis, the empirical likelihood method, control variate method, quantile regression, realized volatility etc., which have been recently developed and are considered to be powerful tools for analyzing the financial data, establishing a new bridge between time series and financial engineering. This book is well suited as a professional reference book on finance, statistics and statistical financial engineering. Readers are expected to have an undergraduate-level knowledge of statistics.
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.
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)
A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers
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.
Application of neuro-fuzzy methods to gamma spectroscopy
Grelle, Austin L.
Nuclear non-proliferation activities are an essential part of national security activities both domestic and abroad. The safety of the public in densely populated environments such as urban areas or large events can be compromised if devices using special nuclear materials are present. Therefore, the prompt and accurate detection of these materials is an important topic of research, in which the identification of normal conditions is also of importance. With gamma-ray spectroscopy, these conditions are identified as the radiation background, which though being affected by a multitude of factors is ever present. Therefore, in nuclear non-proliferation activities the accurate identification of background is important. With this in mind, a method has been developed to utilize aggregate background data to predict the background of a location through the use of an Artificial Neural Network (ANN). After being trained on background data, the ANN is presented with nearby relevant gamma-ray spectroscopy data---as identified by a Fuzzy Inference System - to create a predicted background spectra to compare to a measured spectra. If a significant deviation exists between the predicted and measured data, the method alerts the user such that a more thorough investigation can take place. Research herein focused on data from an urban setting in which the number of false positives was observed to be 28 out of a total of 987, representing 2.94% error. The method therefore currently shows a high rate of false positives given the current configuration, however there are promising steps that can be taken to further minimize this error. With this in mind, the method stands as a potentially significant tool in urban nuclear nonproliferation activities.
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.
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.
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.
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.
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)
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
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.
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)
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...
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.
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)
FPGA implementation of neuro-fuzzy system with improved PSO learning.
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.
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.
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.
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.
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.
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)
Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.
2012-04-01
The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the
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
PREDIKSI PENGGUNA BUS TRANS SARBAGITA DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM
Directory of Open Access Journals (Sweden)
SLAMET SAMSUL HIDAYAT
2013-11-01
Full Text Available Trans Sarbagita is a public transportation services people at Denpasar, Badung, Gianyar and Tabanan. Trans Sarbagita is aimed to resolve a problems caused by accretion volume of vehicles in Bali. This study conducted to forecast the number of Trans Sarbagita passengers in 2013 using ANFIS. The ANFIS system composed by five layers where each layers has a different function and its divide in two phases, i.e. forward and backward phases. The ANFIS uses a hybrid learning algorithm which is a combination of Least Squares Estimator (LSE on forwards phases and Error Backpropagation (EBP on the backward phases. The results show, ANFIS with six inputs with M.F of Pi produces smallest error, compared to seven and eight input and M.F gauss and generalizedbell. Forecast of Trans Sarbagita passenger numbers in 2013 have to fluctuated every day and the average of passenger’s Trans Sarbagita for a day is 1627 passengers with MSE equal to 10210 and MAPE is 4.01%.
PREDIKSI PENGGUNA BUS TRANS SARBAGITA DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM
Directory of Open Access Journals (Sweden)
SLAMET SAMSUL HIDAYAT
2013-08-01
Full Text Available Trans Sarbagita is a public transportation services people at Denpasar, Badung, Gianyar and Tabanan. Trans Sarbagita is aimed to resolve a problems caused by accretion volume of vehicles in Bali. This study conducted to forecast the number of Trans Sarbagita passengers in 2013 using ANFIS. The ANFIS system composed by five layers where each layers has a different function and its divide in two phases, i.e. forward and backward phases. The ANFIS uses a hybrid learning algorithm which is a combination of Least Squares Estimator (LSE on forwards phases and Error Backpropagation (EBP on the backward phases. The results show, ANFIS with six inputs with M.F of Pi produces smallest error, compared to seven and eight input and M.F gauss and generalizedbell. Forecast of Trans Sarbagita passenger numbers in 2013 have to fluctuated every day and the average of passenger’s Trans Sarbagita for a day is 1627 passengers with MSE equal to 10210 and MAPE is 4.01%.
Umam, F.; Budiarto, H.
2018-01-01
Shrimp farming becomes the main commodity of society in Madura Island East Java Indonesia. Because of Madura island has a very extreme weather, farmers have difficulty in keeping the balance of pond water. As a consequence of this condition, there are some farmers experienced losses. In this study an adaptive control system was developed using ANFIS method to control pH balance (7.5-8.5), Temperature (25-31°C), water level (70-120 cm) and Dissolved Oxygen (4-7,5 ppm). Each parameter (pH, temperature, level and DO) is controlled separately but can work together. The output of the control system is in the form of pump activation which provides the antidote to the imbalance that occurs in pond water. The system is built with two modes at once, which are automatic mode and manual mode. The manual control interface based on android which is easy to use.
DEFF Research Database (Denmark)
Justesen, Kristian Kjær; Ehmsen, Mikkel Præstholm; Andersen, John
2012-01-01
This work presents the experimental study and modelling of a methanol reformer system for a high temperature polymer electrolyte membrane (HTPEM) fuel cell stack. The analyzed system is a fully integrated HTPEM fuel cell system with a DC/DC control output able to be used as e.g. a mobile battery...... charger. The advantages of using a HTPEM methanol reformer is that the high quality waste heat can be used as a system heat input to heat and evaporate the input methanol/water mixture which afterwards is catalytically converted into a hydrogen rich gas usable in the high CO tolerant HTPEM fuel cells....... Creating a fuel cell system able to use a well known and easily distributable liquid fuel such as methanol is a good choice in some applications such as range extenders for electric vehicles as an alternative to compressed hydrogen. This work presents a control strategy called Current Correction...
Nuclear power plant transient identification using a neuro-fuzzy inference system
International Nuclear Information System (INIS)
Mol, Antonio Carlos de Abreu; Oliveira, Mauro Vitor de; Santos, Isaac Jose Antonio Luchetti dos; Carvalho, Paulo Victor Rodrigues de; Grecco, Claudio Henrique dos Santos; Auguto, Silas Cordeiro
2005-01-01
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 nuclear power plants. The basis for the identification of a change in the system is that different system faults and anomalies lead to different patterns of evolution of 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. In this work, an approach for the identification of transients is presented, aiming at helping the operator to make a decision relative to the procedure to be followed in situations of accidents/transients at nuclear power plants. In this way, a diagnostic strategy based on hierarchical use artificial neural networks (ANN) for a first level transient diagnose. After the ANN has done a preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. In order to validate the method, a Nuclear Power Plant transient identification problem, comprising postulated accidents, is proposed. Noisy data was used to evaluate the method robustness. The results obtained reveal the ability of the method in dealing with dynamic identification of transients and its reliability degree. (author)
Digital Repository Service at National Institute of Oceanography (India)
Harish, N.; Mandal, S.; Rao, S.; Lokesha
coefficient (CC) and scatter index (SI) for test data are 8.072, 2.841, 0.92, and 0.218 respectively. Comparing with the artificial neural network model, ANFIS yields higher CC and lower SI. From the results it can be concluded that ANFIS can be an efficient...
Diagnosis of rotor fault using neuro-fuzzy inference system | Merabet ...
African Journals Online (AJOL)
Journal of Fundamental and Applied Sciences. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives · Journal Home > Vol 9, No 1 (2017) >. Log in or Register to get access to full text downloads.
Diagnosis of rotor fault using neuro-fuzzy inference system | Merabet ...
African Journals Online (AJOL)
The three-phase induction machines (IM) is large importance and are being widely used as electromechanical system device regarding for their robustness, reliability, and simple design with well developed technologies. This work presents a reliable method for diagnosis and detection of rotor broken bars faults in induction ...
Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system
B. Tutmez (Bulent); Z. Hatipoglu (Z.); U. Kaymak (Uzay)
2006-01-01
textabstractElectrical conductivity is an important indicator for water quality assessment. Since the composition of mineral salts affects the electrical conductivity of groundwater, it is important to understand the relationships between mineral salt composition and electrical conductivity. In this
Modelling electrical conductivity of groundwater using and adaptive neuro-fuzzy inference system
Tutmez, B.; Hatipoglu, Z.; Kaymak, U.
2006-01-01
Electrical conductivity is an important indicator for water quality assessment. Since the composition of mineral salts affects the electrical conductivity of groundwater, it is important to understand the relationships between mineral salt composition and electrical conductivity. In this present
Temperature dependent estimator for load cells using an adaptive neuro-fuzzy inference system
Energy Technology Data Exchange (ETDEWEB)
Lee, K-C [Department of Automation Engineering, National Formosa University, Huwei, Yunlin 63208, Taiwan (China)
2005-01-01
Accurate weighting of pieces in various temperature environments for load cells is a key feature in many industrial applications. This paper proposes a method to achieve high-precision {+-}0.56/3000 grams for a load-cell-based weighting system by using ANFIS. ANFIS is used to model the relationship between the reading of load cells and the actual weight of samples considering temperature-varying effect and nonlinearity of the load cells. The model of the load-cell-based weighting system can accurately estimate the weight of test samples from the load cell reading. The proposed ANFIS-based method is convenient for use and can improve the precision of digital load cell measurement systems. Experiments demonstrate the validity and effectiveness of fuzzy neural networks for modeling of load cells and the results show that the proposed ANFIS-based method outperforms some existing methods in terms of modeling and prediction accuracy.
Razavi Termeh, Seyed Vahid; Kornejady, Aiding; Pourghasemi, Hamid Reza; Keesstra, Saskia
2018-01-01
Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom
Temperature dependent estimator for load cells using an adaptive neuro-fuzzy inference system
International Nuclear Information System (INIS)
Lee, K-C
2005-01-01
Accurate weighting of pieces in various temperature environments for load cells is a key feature in many industrial applications. This paper proposes a method to achieve high-precision ±0.56/3000 grams for a load-cell-based weighting system by using ANFIS. ANFIS is used to model the relationship between the reading of load cells and the actual weight of samples considering temperature-varying effect and nonlinearity of the load cells. The model of the load-cell-based weighting system can accurately estimate the weight of test samples from the load cell reading. The proposed ANFIS-based method is convenient for use and can improve the precision of digital load cell measurement systems. Experiments demonstrate the validity and effectiveness of fuzzy neural networks for modeling of load cells and the results show that the proposed ANFIS-based method outperforms some existing methods in terms of modeling and prediction accuracy
International Nuclear Information System (INIS)
Landeras, Gorka; López, José Javier; Kisi, Ozgur; Shiri, Jalal
2012-01-01
Highlights: ► Solar radiation estimation based on Gene Expression Programming is unexplored. ► This approach is evaluated for the first time in this study. ► Other artificial intelligence models (ANN and ANFIS) are also included in the study. ► New alternatives for solar radiation estimation based on temperatures are provided. - Abstract: Surface incoming solar radiation is a key variable for many agricultural, meteorological and solar energy conversion related applications. In absence of the required meteorological sensors for the detection of global solar radiation it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). A comparison was also made among these techniques and traditional temperature based global solar radiation estimation equations. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SS RMSE ), MAE-based skill score (SS MAE ) and r 2 criterion of Nash and Sutcliffe criteria were used to assess the models’ performances. An ANN (a four-input multilayer perceptron with 10 neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m −2 d −1 of RMSE). The ability of GEP approach to model global solar radiation based on daily atmospheric variables was found to be satisfactory.
Hoell, Simon; Omenzetter, Piotr
2016-04-01
Fueled by increasing demand for carbon neutral energy, erections of ever larger wind turbines (WTs), with WT blades (WTBs) with higher flexibilities and lower buckling capacities lead to increasing operation and maintenance costs. This can be counteracted with efficient structural health monitoring (SHM), which allows scheduling maintenance actions according to the structural state and preventing dramatic failures. The present study proposes a novel multi-step approach for vibration-based structural damage localization and severity estimation for application in operating WTs. First, partial autocorrelation coefficients (PACCs) are estimated from vibrational responses. Second, principal component analysis is applied to PACCs from the healthy structure in order to calculate scores. Then, the scores are ranked with respect to their ability to differentiate different damage scenarios. This ranking information is used for constructing hierarchical adaptive neuro-fuzzy inference systems (HANFISs), where cross-validation is used to identify optimal numbers of hierarchy levels. Different HANFISs are created for the purposes of structural damage localization and severity estimation. For demonstrating the applicability of the approach, experimental data are superimposed with signals from numerical simulations to account for characteristics of operational noise. For the physical experiments, a small scale WTB is excited with a domestic fan and damage scenarios are introduced non-destructively by attaching small masses. Numerical simulations are also performed for a representative fully functional small WT operating in turbulent wind. The obtained results are promising for future applications of vibration-based SHM to facilitate improved safety and reliability of WTs at lower costs.
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)
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.
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.
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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.
Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm
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.
New neuro-fuzzy system-based holey polymer fibers drawing process
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.
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.
Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning
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.
Performance analysis of electronic power transformer based on neuro-fuzzy controller.
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.
Macroscopic Rock Texture Image Classification Using a Hierarchical Neuro-Fuzzy Class Method
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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.
Phase Angle Control of Three Level Inverter Based D-STATCOM Using Neuro-Fuzzy Controller
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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.
Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO
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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.
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
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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.
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
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...
Short-term and long-term thermal prediction of a walking beam furnace using neuro-fuzzy techniques
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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.
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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.
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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.
IMAGINE: Interstellar MAGnetic field INference Engine
Steininger, Theo
2018-03-01
IMAGINE (Interstellar MAGnetic field INference Engine) performs inference on generic parametric models of the Galaxy. The modular open source framework uses highly optimized tools and technology such as the MultiNest sampler (ascl:1109.006) and the information field theory framework NIFTy (ascl:1302.013) to create an instance of the Milky Way based on a set of parameters for physical observables, using Bayesian statistics to judge the mismatch between measured data and model prediction. The flexibility of the IMAGINE framework allows for simple refitting for newly available data sets and makes state-of-the-art Bayesian methods easily accessible particularly for random components of the Galactic magnetic field.
Milovančević, Miloš; Nikolić, Vlastimir; Anđelković, Boban
2017-01-01
Vibration-based structural health monitoring is widely recognized as an attractive strategy for early damage detection in civil structures. Vibration monitoring and prediction is important for any system since it can save many unpredictable behaviors of the system. If the vibration monitoring is properly managed, that can ensure economic and safe operations. Potentials for further improvement of vibration monitoring lie in the improvement of current control strategies. One of the options is the introduction of model predictive control. Multistep ahead predictive models of vibration are a starting point for creating a successful model predictive strategy. For the purpose of this article, predictive models of are created for vibration monitoring of planetary power transmissions in pellet mills. The models were developed using the novel method based on ANFIS (adaptive neuro fuzzy inference system). The aim of this study is to investigate the potential of ANFIS for selecting the most relevant variables for predictive models of vibration monitoring of pellet mills power transmission. The vibration data are collected by PIC (Programmable Interface Controller) microcontrollers. The goal of the predictive vibration monitoring of planetary power transmissions in pellet mills is to indicate deterioration in the vibration of the power transmissions before the actual failure occurs. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of vibration monitoring. It was also used to select the minimal input subset of variables from the initial set of input variables - current and lagged variables (up to 11 steps) of vibration. The obtained results could be used for simplification of predictive methods so as to avoid multiple input variables. It was preferable to used models with less inputs because of overfitting between training and testing data. While the obtained results are promising, further work is
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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
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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.
Neuro-fuzzy model for estimating race and gender from geometric distances of human face across pose
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.
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.
Aproximación neuro-fuzzy para identificación de señales viales mediante tecnología infrarroja
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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
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
A Comparative Analysis of Fuzzy Inference Engines in Context of ...
African Journals Online (AJOL)
Fuzzy inference engine has found successful applications in a wide variety of fields, such as automatic control, data classification, decision analysis, expert engines, time series prediction, robotics, pattern recognition, etc. This paper presents a comparative analysis of three fuzzy inference engines, max-product, max-min ...
Pradhan, Biswajeet
2013-02-01
The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e
Tomato grading system using machine vision technology and neuro-fuzzy networks (ANFIS
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H Izadi
2016-04-01
Full Text Available Introduction: The quality of agricultural products is associated with their color, size and health, grading of fruits is regarded as an important step in post-harvest processing. In most cases, manual sorting inspections depends on available manpower, time consuming and their accuracy could not be guaranteed. Machine Vision is known to be a useful tool for external features measurement (e.g. size, shape, color and defects and in recent century, Machine Vision technology has been used for shape sorting. The main purpose of this study was to develop new method for tomato grading and sorting using Neuro-fuzzy system (ANFIS and to compare the accuracies of the ANFIS predicted results with those suggested by a human expert. Materials and Methods: In this study, a total of 300 image of tomatoes (Rev ground was randomly harvested, classified in 3 ripeness stage, 3 sizes and 2 health. The grading and sorting mechanism consisted of a lighting chamber (cloudy sky, lighting source and a digital camera connected to a computer. The images were recorded in a special chamber with an indirect radiation (cloudy sky with four florescent lampson each sides and camera lens was entire to lighting chamber by a hole which was only entranced to outer and covered by a camera lens. Three types of features were extracted from final images; Shap, color and texture. To receive these features, we need to have images both in color and binary format in procedure shown in Figure 1. For the first group; characteristics of the images were analysis that could offer information an surface area (S.A., maximum diameter (Dmax, minimum diameter (Dmin and average diameters. Considering to the importance of the color in acceptance of food quality by consumers, the following classification was conducted to estimate the apparent color of the tomato; 1. Classified as red (red > 90% 2. Classified as red light (red or bold pink 60-90% 3. Classified as pink (red 30-60% 4. Classified as Turning
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.
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.
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.
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.
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)
DEFF Research Database (Denmark)
Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza
2013-01-01
In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous...... of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other’s output. The models take this into account using...... an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model which is adapted to fit the measured performance of the H3-350 module. All the individual parts of the model are verified...
DEFF Research Database (Denmark)
Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza
2014-01-01
In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous...... of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other’s output. The models take this into account using...... an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model which is adapted to fit the measured performance of the H3-350 module. All the individual parts of the model are verified...
Mohd Yunos, Zuriahati; Shamsuddin, Siti Mariyam; Ismail, Noriszura; Sallehuddin, Roselina
2013-04-01
Artificial neural network (ANN) with back propagation algorithm (BP) and ANFIS was chosen as an alternative technique in modeling motor insurance claims. In particular, an ANN and ANFIS technique is applied to model and forecast the Malaysian motor insurance data which is categorized into four claim types; third party property damage (TPPD), third party bodily injury (TPBI), own damage (OD) and theft. This study is to determine whether an ANN and ANFIS model is capable of accurately predicting motor insurance claim. There were changes made to the network structure as the number of input nodes, number of hidden nodes and pre-processing techniques are also examined and a cross-validation technique is used to improve the generalization ability of ANN and ANFIS models. Based on the empirical studies, the prediction performance of the ANN and ANFIS model is improved by using different number of input nodes and hidden nodes; and also various sizes of data. The experimental results reveal that the ANFIS model has outperformed the ANN model. Both models are capable of producing a reliable prediction for the Malaysian motor insurance claims and hence, the proposed method can be applied as an alternative to predict claim frequency and claim severity.
Meau, Yeong Pong; Ibrahim, Fatimah; Narainasamy, Selvanathan A L; Omar, Razali
2006-05-01
This study presents the development of a hybrid system consisting of an ensemble of Extended Kalman Filter (EKF) based Multi Layer Perceptron Network (MLPN) and a one-pass learning Fuzzy Inference System using Look-up Table Scheme for the recognition of electrocardiogram (ECG) signals. This system can distinguish various types of abnormal ECG signals such as Ventricular Premature Cycle (VPC), T wave inversion (TINV), ST segment depression (STDP), and Supraventricular Tachycardia (SVT) from normal sinus rhythm (NSR) ECG signal.
A Comparative Analysis of Fuzzy Inference Engines in Context of ...
African Journals Online (AJOL)
PROF. O. E. OSUAGWU
Fuzzy Inference engine is an important part of reasoning systems capable of extracting correct conclusions from ... is known as the inference, or rule definition portion, of fuzzy .... minimal set of decision rules based on input- ... The study uses Mamdani FIS model and. Sugeno FIS ... control of induction motor drive. [18] study.
Automated Flight Safety Inference Engine (AFSIE) System, Phase I
National Aeronautics and Space Administration — We propose to develop an innovative Autonomous Flight Safety Inference Engine (AFSIE) system to autonomously and reliably terminate the flight of an errant launch...
Applied to neuro-fuzzy models for signal validation in Angra 1 nuclear power plant
International Nuclear Information System (INIS)
Oliveira, Mauro Vitor de
1999-06-01
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)
Estimation of dew point temperature using neuro-fuzzy and neural network techniques
Kisi, Ozgur; Kim, Sungwon; Shiri, Jalal
2013-11-01
This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T mean and R H variables.
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
Erratum: Erratum to Central European Journal of Engineering, Volume 4, Issue 1
Kumar, M. Ajay; Srikanth, N. V.
2014-06-01
Paper by M. Ajay Kumar, N. V. Srikanth, et al. "An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions" in Volume 4, Issue 1, 27-38/March 2014 doi: 10.2478/s13531-013-0143-4 contains an error in the title. The correct title is presented below
F-OWL: An Inference Engine for Semantic Web
Zou, Youyong; Finin, Tim; Chen, Harry
2004-01-01
Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.
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.
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
International Nuclear Information System (INIS)
Yokobayashi, Masao; Yoshida, Kazuo; Kohsaka, Atsuo; Yamamoto, Minoru.
1984-11-01
This report describes an inference engine IERIAS which has been devoloped for a diagnostic system to identify the cause and type of an abnormal transient of a reactor plant. This system using knowledge engineering technique consists of a knowledge base and an inference engine. The inference engine IERIAS is designed so as to treat time-varying data of a plant. The major features of IERIAS are ; (1) histroy of transients can be treated, (2) knowledge base can be divided into some knowledge units, (3) program language UTILISP is used which is suitable for symbolic data manipulation. Inference was made using IERIAS with a knowledge base which was created from simulated results of various transients by a PWR plant simulator. The results showed a good applicability of IERIAS for reactor diagnosis. (author)
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.
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.
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.
Digital Repository Service at National Institute of Oceanography (India)
Patil, S.G.; Mandal, S.; Hegde, A.V.; Alavandar, S.
System (ANFIS), an implementation of a representative fuzzy inference system using a back-propagation neural network like structure, with limited mathematical representation of the system, is developed. An ANFIS is trained on the data set obtained from...
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
Rustamov, Samir; Mustafayev, Elshan; Clements, Mark A.
2018-04-01
The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM) can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to nonusage of lexical or syntactic analysis in classification process.
Takdastan, Afshin; Mirzabeygi Radfard, Majid; Yousefi, Mahmood; Abbasnia, Abbas; Khodadadia, Rouhollah; Soleimani, Hamed; Mahvi, Amir Hossein; Naghan, Davood Jalili
2018-06-01
According to World Health Organization guidelines, corrosion control is an important aspect of safe drinking-water supplies. Water always includes ingredients, dissolved gases and suspended materials. Although some of these water ingredients is indispensable for human beings, these elements more than permissible limits, could be endanger human health. The aim of this study is to assess physical and chemical parameters of drinking water in the rural areas of Lordegan city, also to determine corrosion indices. This cross-sectional study has carried out with 141 taken samples during 2017 with 13 parameters, which has been analyzed based on standard method and to estimate the water quality indices from groundwater using ANFIS. Also with regard to standard conditions, results of this paper are compared with Environmental Protection Agency and Iran national standards. Five indices, Ryznar Stability Index (RSI), Langlier Saturation Index (LSI), Larson-Skold Index (LS), Puckorius Scaling Index (PSI), and Aggressive Index (AI) programmed by using Microsoft Excel software. Owing to its simplicity, the program, can easily be used by researchers and operators. Parameters included Sulfate, Sodium, Chloride, and Electrical Conductivity respectively were 13.5, 28, 10.5, and 15% more than standard level. The amount of Nitrate, in 98% of cases were in permissible limits and about 2% were more than standard level. Result of presented research indicate that water is corrosive at 10.6%,89.4%,87.2%,59.6% and 14.9% of drinking water supply reservoirs, according to LSI, RSI, PSI, LS and AI, respectively.
Directory of Open Access Journals (Sweden)
Rustamov Samir
2018-04-01
Full Text Available The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to nonusage of lexical or syntactic analysis in classification process.
Directory of Open Access Journals (Sweden)
Evanita Evanita
2016-04-01
Full Text Available Di Indonesia kepadatan arus lalu lintas terjadi pada jam berangkat dan pulang kantor, hari-hari libur panjang atau hari-hari besar nasional terutama saat hari raya Idul Fitri (lebaran. Mudik sudah menjadi tradisi bagi masyarakat Indonesia yang ditunggu-tunggu menjelang lebaran, berbondong-bondong untuk pulang ke kampung halaman untuk bertemu dan berkumpul dengan keluarga. Kegiatan rutin tahunan ini banyak di lakukan khususnya bagi masyarakat kota-kota besar seperti Jakarta, dimana diketahui bahwa Jakarta adalah Ibu kota negara Republik Indonesia dan menjadi tujuan merantau untuk mencari pekerjaan yang lebih layak yang merupakan harapan besar bagi masyarakat desa. Volume kendaraan bertambah sejak 7 hari menjelang lebaran sampai 7 hari setelah lebaran tiap tahunnya terutama pada arah keluar dan masuk wilayah Jawa Tengah yang banyak menjadi tujuan mudik. Volume kendaraan saat arus mudik yang selalu meningkat inilah yang akan diteliti lebih lanjut dengan metode ANFIS agar dapat menjadi alternatif solusi langkah apa yang akan dilakukan di tahun selanjutnya agar pelayanan lalu lintas, kemacetan panjang dan angka kecelakaan berkurang. Dengan input parameter ANFIS yang digunakan yaitu pengclusteran hingga 5 cluster, epoch 100, error goal 0 diperoleh performa terbaik ANFIS dengan K-Means clustering yang terbagi menjadi 3 cluster, epoch terbaik sebesar 20 dengan RMSE Training terbaik sebesar 0,1198, RMSE Testing terbaik sebesar 0,0282 dan waktu proses tersingkat sebesar 0,0695.Selanjutnya hasil prediksi diharapkan dapat bermanfaat menjadi alternatif solusi langkah apa yang akan dilakukan di tahun selanjutnya agar pelayanan lalu lintas lebih baik lagi. Kata kunci: angkutan lebaran, Jawa Tengah, ANFIS.
Directory of Open Access Journals (Sweden)
Ali Hadi Abdulwahid
2018-04-01
Full Text Available This paper addresses the energy challenges related to the weak protection of renewable energy from reverse energy flow and expanding access to high-quality energy at the same time. Furthermore, this paper focuses on participation in the global transition to clean and low-carbon energy systems. Moreover, the increased demand for renewable energy seems to likely depend on whether it will be possible to greatly accelerate rates of progress toward increased efficiency, de-carbonization, greater generating diversity and lower pollutant emissions. This paper focuses on the protection of renewable energy technologies because they can be particularly attractive in dispersed areas and therefore, represent an important option for rural areas that lack electrical energy and distribution infrastructure. This paper proposes an improved protection device for a reverse power protection system using a new intelligent decision support system (IDSS. The IDSS is a support system for decision making, which makes extensive use of artificial intelligence (AI techniques. The new method integrates the powerful specification for neural networks and fuzzy inference systems. The main advantage of this method is that it causes a decrease in the steady state oscillation for the reverse power relay. In addition, the proposed method has the ability to monitor extreme environmental conditions. The generator can be converted into a motor when the steam supply to a turbine is interrupted while the generator is still connected to a grid (or operates in parallel with another generator. As a result, the generator will become a synchronous motor and will actually cause significant mechanical damage. The reverse energy protection device should be included in the generator protection scheme. Smart grids use communication networks with sophisticated algorithms to ensure coordination between protection systems. ZigBee is a newly developed technology that can be used in wireless sensor
DEFF Research Database (Denmark)
Møller, Jesper
2010-01-01
Chapter 9: This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based on a maximum likelihood or Bayesian approach combined with markov chain Monte Carlo...... (MCMC) techniques. Due to space limitations the focus is on spatial point processes....
A Modular Artificial Intelligence Inference Engine System (MAIS) for support of on orbit experiments
Hancock, Thomas M., III
1994-01-01
This paper describes a Modular Artificial Intelligence Inference Engine System (MAIS) support tool that would provide health and status monitoring, cognitive replanning, analysis and support of on-orbit Space Station, Spacelab experiments and systems.
DEFF Research Database (Denmark)
Møller, Jesper
(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.1 with the ......(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.......1 with the title ‘Inference'.) This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods using Markov chain Monte Carlo (MCMC) simulations. Due to space limitations the focus...
Chakraborty, Amitav; Roy, Sumit; Banerjee, Rahul
2018-03-01
This experimental work highlights the inherent capability of an adaptive-neuro fuzzy inference system (ANFIS) based model to act as a robust system identification tool (SIT) in prognosticating the performance and emission parameters of an existing diesel engine running of diesel-LPG dual fuel mode. The developed model proved its adeptness by successfully harnessing the effects of the input parameters of load, injection duration and LPG energy share on output parameters of BSFCEQ, BTE, NOX, SOOT, CO and HC. Successive evaluation of the ANFIS model, revealed high levels of resemblance with the already forecasted ANN results for the same input parameters and it was evident that similar to ANN, ANFIS also has the innate ability to act as a robust SIT. The ANFIS predicted data harmonized the experimental data with high overall accuracy. The correlation coefficient (R) values are stretched in between 0.99207 to 0.999988. The mean absolute percentage error (MAPE) tallies were recorded in the range of 0.02-0.173% with the root mean square errors (RMSE) in acceptable margins. Hence the developed model is capable of emulating the actual engine parameters with commendable ranges of accuracy, which in turn would act as a robust prediction platform in the future domains of optimization.
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.
PIA: An Intuitive Protein Inference Engine with a Web-Based User Interface.
Uszkoreit, Julian; Maerkens, Alexandra; Perez-Riverol, Yasset; Meyer, Helmut E; Marcus, Katrin; Stephan, Christian; Kohlbacher, Oliver; Eisenacher, Martin
2015-07-02
Protein inference connects the peptide spectrum matches (PSMs) obtained from database search engines back to proteins, which are typically at the heart of most proteomics studies. Different search engines yield different PSMs and thus different protein lists. Analysis of results from one or multiple search engines is often hampered by different data exchange formats and lack of convenient and intuitive user interfaces. We present PIA, a flexible software suite for combining PSMs from different search engine runs and turning these into consistent results. PIA can be integrated into proteomics data analysis workflows in several ways. A user-friendly graphical user interface can be run either locally or (e.g., for larger core facilities) from a central server. For automated data processing, stand-alone tools are available. PIA implements several established protein inference algorithms and can combine results from different search engines seamlessly. On several benchmark data sets, we show that PIA can identify a larger number of proteins at the same protein FDR when compared to that using inference based on a single search engine. PIA supports the majority of established search engines and data in the mzIdentML standard format. It is implemented in Java and freely available at https://github.com/mpc-bioinformatics/pia.
Statistics for nuclear engineers and scientists. Part 1. Basic statistical inference
Energy Technology Data Exchange (ETDEWEB)
Beggs, W.J.
1981-02-01
This report is intended for the use of engineers and scientists working in the nuclear industry, especially at the Bettis Atomic Power Laboratory. It serves as the basis for several Bettis in-house statistics courses. The objectives of the report are to introduce the reader to the language and concepts of statistics and to provide a basic set of techniques to apply to problems of the collection and analysis of data. Part 1 covers subjects of basic inference. The subjects include: descriptive statistics; probability; simple inference for normally distributed populations, and for non-normal populations as well; comparison of two populations; the analysis of variance; quality control procedures; and linear regression analysis.
Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures
Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland
1998-01-01
Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.
International Nuclear Information System (INIS)
Azadeh, A.; Asadzadeh, S.M.; Saberi, M.; Nadimi, V.; Tajvidi, A.; Sheikalishahi, M.
2011-01-01
Highlights: → This paper presents a unique approach for long-term natural gas consumption estimation. → It is applied to selected Arab countries to show its superiority and applicability. → It may be used for other real cases for optimum gas consumption estimation. → It is compared with current studies to show its advantages. → It is capable of dealing with complexity, ambiguity, fuzziness, and randomness. -- Abstract: This paper presents an adaptive network-based fuzzy inference system-stochastic frontier analysis (ANFIS-SFA) approach for long-term natural gas (NG) consumption prediction and analysis of the behavior of NG consumption. The proposed models consist of input variables of Gross Domestic Product (GDP) and population (POP). Six distinct models based on different inputs are defined. All of trained ANFIS are then compared with respect to mean absolute percentage error (MAPE). To meet the best performance of the intelligent based approaches, data are pre-processed (scaled) and finally the outputs are post-processed (returned to its original scale). To show the applicability and superiority of the integrated ANFIS-SFA approach, gas consumption in four Middle Eastern countries i.e. Bahrain, Saudi Arabia, Syria, and United Arab Emirates is forecasted and analyzed based on the data of the time period 1980-2007. With the aid of autoregressive model, GDP and population are projected for the period 2008-2015. These projected data are used as the input of ANFIS model to predict the gas consumption in the selected countries for 2008-2015. SFA is then used to examine the behavior of gas consumption in the past and also to make insights for the forthcoming years. The ANFIS-SFA approach is capable of dealing with complexity, uncertainty, and randomness as well as several other unique features discussed in this paper.
Automatic Diagnosis of Fetal Heart Rate: Comparison of Different Methodological Approaches
National Research Council Canada - National Science Library
Magenes, G
2001-01-01
.... A Multilayer Perception (MLP) neural network and an Adaptive Neuro-Fuzzy Inference System (ANFIS) were compared with classical statistical methods. Both the neural and neuro-fuzzy approaches seem to give better results than any tested statistical classifier.
Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering
Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland
2000-01-01
Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.
Audain, Enrique; Uszkoreit, Julian; Sachsenberg, Timo; Pfeuffer, Julianus; Liang, Xiao; Hermjakob, Henning; Sanchez, Aniel; Eisenacher, Martin; Reinert, Knut; Tabb, David L; Kohlbacher, Oliver; Perez-Riverol, Yasset
2017-01-06
In mass spectrometry-based shotgun proteomics, protein identifications are usually the desired result. However, most of the analytical methods are based on the identification of reliable peptides and not the direct identification of intact proteins. Thus, assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is a critical step in proteomics research. Currently, different protein inference algorithms and tools are available for the proteomics community. Here, we evaluated five software tools for protein inference (PIA, ProteinProphet, Fido, ProteinLP, MSBayesPro) using three popular database search engines: Mascot, X!Tandem, and MS-GF+. All the algorithms were evaluated using a highly customizable KNIME workflow using four different public datasets with varying complexities (different sample preparation, species and analytical instruments). We defined a set of quality control metrics to evaluate the performance of each combination of search engines, protein inference algorithm, and parameters on each dataset. We show that the results for complex samples vary not only regarding the actual numbers of reported protein groups but also concerning the actual composition of groups. Furthermore, the robustness of reported proteins when using databases of differing complexities is strongly dependant on the applied inference algorithm. Finally, merging the identifications of multiple search engines does not necessarily increase the number of reported proteins, but does increase the number of peptides per protein and thus can generally be recommended. Protein inference is one of the major challenges in MS-based proteomics nowadays. Currently, there are a vast number of protein inference algorithms and implementations available for the proteomics community. Protein assembly impacts in the final results of the research, the quantitation values and the final claims in the research manuscript. Even though protein
International Nuclear Information System (INIS)
Mousavifard, S.M.; Attar, M.M.; Ghanbari, A.; Dadgar, M.
2015-01-01
Highlights: • Film formation of Zr-based conversion coating under different conditions was investigated. • We study the effect of some parameters on anticorrosion performance of conversion coating. • Optimization of processing conditions for surface treatment of galvanized steel was obtained. • Modeling and predicting corrosion current density of treated surfaces was performed using ANN and ANFIS. - Abstract: A nano-ceramic Zr-based conversion solution was prepared and optimization of Zr concentration, pH, temperature and immersion time for the treatment of hot-dip galvanized steel (HDG) was performed. SEM microscopy was utilized to investigate the microstructure and film formation of the layer and the anticorrosion performance of conversion coating was studied using polarization test. Artificial intelligence systems (ANN and ANFIS) were applied on the data obtained from polarization test and the models for predicting corrosion current density values were attained. The outcome of these models showed proper predictability of the methods. The influence of input parameters was discussed and the optimized conditions for Zr-based conversion layer formation on the galvanized steel were obtained as follows: pH 3.8–4.5, Zr concentration of about 100 ppm, ambient temperature and immersion time of about 90 s
Energy Technology Data Exchange (ETDEWEB)
Mousavifard, S.M. [Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran (Iran, Islamic Republic of); Attar, M.M., E-mail: attar@aut.ac.ir [Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran (Iran, Islamic Republic of); Ghanbari, A. [Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran (Iran, Islamic Republic of); Dadgar, M. [Textile Engineering Department, Neyshabur University, Neyshabur (Iran, Islamic Republic of)
2015-08-05
Highlights: • Film formation of Zr-based conversion coating under different conditions was investigated. • We study the effect of some parameters on anticorrosion performance of conversion coating. • Optimization of processing conditions for surface treatment of galvanized steel was obtained. • Modeling and predicting corrosion current density of treated surfaces was performed using ANN and ANFIS. - Abstract: A nano-ceramic Zr-based conversion solution was prepared and optimization of Zr concentration, pH, temperature and immersion time for the treatment of hot-dip galvanized steel (HDG) was performed. SEM microscopy was utilized to investigate the microstructure and film formation of the layer and the anticorrosion performance of conversion coating was studied using polarization test. Artificial intelligence systems (ANN and ANFIS) were applied on the data obtained from polarization test and the models for predicting corrosion current density values were attained. The outcome of these models showed proper predictability of the methods. The influence of input parameters was discussed and the optimized conditions for Zr-based conversion layer formation on the galvanized steel were obtained as follows: pH 3.8–4.5, Zr concentration of about 100 ppm, ambient temperature and immersion time of about 90 s.
Neuro-fuzzy model of homocysteine metabolism
Indian Academy of Sciences (India)
In view of well-documented association of hyperhomocysteinaemia with a wide spectrum of diseases and higher incidence of vitamin deficiencies in Indians, we proposed a mathematical model to forecast the role of demographic and geneticvariables in influencing homocysteine metabolism and investigated the influence ...
Neuro-fuzzy model of homocysteine metabolism
Indian Academy of Sciences (India)
To conclude, polymorphisms in genes regulating remethylation of homocysteine strongly influence homocysteine levels. The restoration of one-carbon homeostasis by SHMT1 C1420T or increased flux of folate towards remethylation due to TYMS 5'-UTR 28 bp tandem repeat or nonvegetariandiet can lower homocysteine ...
Neuro-fuzzy model of homocysteine metabolism
Indian Academy of Sciences (India)
SHAIK Mohammad Naushad
2017-12-08
Dec 8, 2017 ... Homocysteine is a nondietary amino acid, which is the byproduct of ... wide spectrum of diseases such as recurrent pregnancy loss (Govindaiah et al. ... A2756G, MTRR A66G were reported in the folate metabolic pathway ...
Directory of Open Access Journals (Sweden)
Iman Raeesi Vanani
2015-03-01
Full Text Available The main goal of research is designing an adaptive nuero-fuzzy inference system for evaluating the implementation of business intelligence systems in software industry. Iranian software development organizations have been facing a lot of problems in case of implementing business intelligence systems. This system would be helpful in recognizing the conditions and prerequisites of success or failure. Organizations can recalculate the neuro-fuzzy system outputs with some considerations on various inputs to figure out which inputs have the most effect on the implementation outputs. By resolving the problems on inputs, organizations can achieve a better level of implementation success. The designed system has been trained by a data set and afterwards, it has been evaluated. The trained system has reached the error value of 0.08. Eventually, some recommendations have been provided for software development firms on the areas that might need more considerations and improvements.
Portable inference engine: An extended CLIPS for real-time production systems
Le, Thach; Homeier, Peter
1988-01-01
The present C-Language Integrated Production System (CLIPS) architecture has not been optimized to deal with the constraints of real-time production systems. Matching in CLIPS is based on the Rete Net algorithm, whose assumption of working memory stability might fail to be satisfied in a system subject to real-time dataflow. Further, the CLIPS forward-chaining control mechanism with a predefined conflict resultion strategy may not effectively focus the system's attention on situation-dependent current priorties, or appropriately address different kinds of knowledge which might appear in a given application. Portable Inference Engine (PIE) is a production system architecture based on CLIPS which attempts to create a more general tool while addressing the problems of real-time expert systems. Features of the PIE design include a modular knowledge base, a modified Rete Net algorithm, a bi-directional control strategy, and multiple user-defined conflict resolution strategies. Problems associated with real-time applications are analyzed and an explanation is given for how the PIE architecture addresses these problems.
Energy Technology Data Exchange (ETDEWEB)
Tremmel, H.G.; Schlager, H.; Konopka, P.; Schulte, P. [Deutsche Forschungsanstalt fuer Luft- und Raumfahrt e.V. (DLR), Wessling (Germany). Inst. fuer Physik der Atmosphaere; Arnold, F.; Klemm, M.; Droste-Franke, B. [Max-Planck-Institut fuer Kernphysik, Heidelberg (Germany)
1997-06-01
NO{sub y} (NO, HNO{sub 2} and HNO{sub 3}) exhaust emissions in the near-field plume of two B747 jet airliners cruising in the upper troposphere were measured in situ using the DLR Falcon research aircraft. In addition CO{sub 2} was measured providing exhaust plume dilution rates for the species. The observations were used to estimate the initial OH concentration and NO{sub 2}/NO{sub x} ratio at the engine exit and the combustor exit by back calculations using a chemistry box model. From the two different plume events, and using two different model simulation modes in each case, we inferred OH emission indices EI(OH) = 0.32-0.39 g/kg fuel (OH{sub 0} = 9-14.4 ppmv) and (NO{sub 2}/NO{sub x}){sub 0} = 0.12-0.17. Furthermore, our results indicate that the chemistry of the exhaust species during the short period between the combustion chamber exit and the engine exit must be considered, because OH is already consumed to a great extent in this engine section, due to conversion to HNO{sub 2} and HNO{sub 3}. For the engines discussed here, the modeled OH concentration between combustor exit und engine exit decreases by a factor of about 350, leading to OH concentrations of 1-2.10{sup 12} molec/cm{sup 3} at the engine exit. (orig.) 45 refs.
International Nuclear Information System (INIS)
Quigley, John; Walls, Lesley
2011-01-01
Mixing Bayes and Empirical Bayes inference provides reliability estimates for variant system designs by using relevant failure data - observed and anticipated - about engineering changes arising due to modification and innovation. A coherent inference framework is proposed to predict the realization of engineering concerns during product development so that informed decisions can be made about the system design and the analysis conducted to prove reliability. The proposed method involves combining subjective prior distributions for the number of engineering concerns with empirical priors for the non-parametric distribution of time to realize these concerns in such a way that we can cross-tabulate classes of concerns to failure events within time partitions at an appropriate level of granularity. To support efficient implementation, a computationally convenient hypergeometric approximation is developed for the counting distributions appropriate to our underlying stochastic model. The accuracy of our approximation over first-order alternatives is examined, and demonstrated, through an evaluation experiment. An industrial application illustrates model implementation and shows how estimates can be updated using information arising during development test and analysis.
Directory of Open Access Journals (Sweden)
Daniel Lobo
2015-06-01
Full Text Available Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method
Topuz, Emel; van Gestel, Cornelis A M
2016-01-01
The usage of Engineered Nanoparticles (ENPs) in consumer products is relatively new and there is a need to conduct environmental risk assessment (ERA) to evaluate their impacts on the environment. However, alternative approaches are required for ERA of ENPs because of the huge gap in data and knowledge compared to conventional pollutants and their unique properties that make it difficult to apply existing approaches. This study aims to propose an ERA approach for ENPs by integrating Analytical Hierarchy Process (AHP) and fuzzy inference models which provide a systematic evaluation of risk factors and reducing uncertainty about the data and information, respectively. Risk is assumed to be the combination of occurrence likelihood, exposure potential and toxic effects in the environment. A hierarchy was established to evaluate the sub factors of these components. Evaluation was made with fuzzy numbers to reduce uncertainty and incorporate the expert judgements. Overall score of each component was combined with fuzzy inference rules by using expert judgements. Proposed approach reports the risk class and its membership degree such as Minor (0.7). Therefore, results are precise and helpful to determine the risk management strategies. Moreover, priority weights calculated by comparing the risk factors based on their importance for the risk enable users to understand which factor is effective on the risk. Proposed approach was applied for Ag (two nanoparticles with different coating) and TiO2 nanoparticles for different case studies. Results verified the proposed benefits of the approach. Copyright © 2016 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Ozgun Akcay
2015-10-01
Full Text Available Unmanned Aerial Systems (UAS are now capable of gathering high-resolution data, therefore, landslides can be explored in detail at larger scales. In this research, 132 aerial photographs were captured, and 85,456 features were detected and matched automatically using UAS photogrammetry. The root mean square (RMS values of the image coordinates of the Ground Control Points (GPCs varied from 0.521 to 2.293 pixels, whereas maximum RMS values of automatically matched features was calculated as 2.921 pixels. Using the 3D point cloud, which was acquired by aerial photogrammetry, the raster datasets of the aspect, slope, and maximally stable extremal regions (MSER detecting visual uniformity, were defined as three variables, in order to reason fissure structures on the landslide surface. In this research, an Adaptive Neuro Fuzzy Inference System (ANFIS and a Logistic Regression (LR were implemented using training datasets to infer fissure data appropriately. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC curves and by calculating the area under the ROC curve (AUC. The experiments exposed that high-resolution imagery is an indispensable data source to model and validate landslide fissures appropriately.
Lachmann, Alexander; Giorgi, Federico M; Lopez, Gonzalo; Califano, Andrea
2016-07-15
The accurate reconstruction of gene regulatory networks from large scale molecular profile datasets represents one of the grand challenges of Systems Biology. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) represents one of the most effective tools to accomplish this goal. However, the initial Fixed Bandwidth (FB) implementation is both inefficient and unable to deal with sample sets providing largely uneven coverage of the probability density space. Here, we present a completely new implementation of the algorithm, based on an Adaptive Partitioning strategy (AP) for estimating the Mutual Information. The new AP implementation (ARACNe-AP) achieves a dramatic improvement in computational performance (200× on average) over the previous methodology, while preserving the Mutual Information estimator and the Network inference accuracy of the original algorithm. Given that the previous version of ARACNe is extremely demanding, the new version of the algorithm will allow even researchers with modest computational resources to build complex regulatory networks from hundreds of gene expression profiles. A JAVA cross-platform command line executable of ARACNe, together with all source code and a detailed usage guide are freely available on Sourceforge (http://sourceforge.net/projects/aracne-ap). JAVA version 8 or higher is required. califano@c2b2.columbia.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
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)
Streamflow Forecasting Using Nuero-Fuzzy Inference System
Nanduri, U. V.; Swain, P. C.
2005-12-01
The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A
Energy Technology Data Exchange (ETDEWEB)
Sadeh, Javad; Afradi, Hamid [Electrical Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, P.O. Box: 91775-1111, Mashhad (Iran)
2009-11-15
This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines. (author)
2012-01-01
The volume includes a set of selected papers extended and revised from the International Conference on Informatics, Cybernetics, and Computer Engineering. Intelligent control is a class of control techniques, that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. Intelligent control can be divided into the following major sub-domains: Neural network control Bayesian control Fuzzy (logic) control Neuro-fuzzy control Expert Systems Genetic control Intelligent agents (Cognitive/Conscious control) New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them. Networks may be classified according to a wide variety of characteristics such as medium used to transport the data, communications protocol used, scale, topology, organizational scope, etc. ICCE 2011 Volume 1 is to provide a forum for researchers, educators, engi...
Directory of Open Access Journals (Sweden)
Amir Ghanei
2017-10-01
Full Text Available In this study, the effects of nano-CuO (NC on engineering properties of fibre-reinforced mortars incorporating metakaolin (MK were investigated. The effects of polypropylene fibre (PP were also examined. A total of twenty-six mixtures were prepared. The experimental results were compared with numerical results obtained by adaptive neuro-fuzzy inference system (ANFIS and Primal Estimated sub-GrAdient Solver for SVM (Pegasos algorithm. Scanning Electron Microscope (SEM was also employed to investigate the microstructure of the cement matrix. The mechanical test results showed that both compressive and flexural strengths of cement mortars decreased with the increase of MK content, however the strength values increased significantly with increasing NC content in the mixture. The water absorption of samples decreased remarkably with increasing NC particles in the mixture. When PP fibres were added, the strengths of cement mortars were further enhanced accompanied with lower water absorption values. The addition of 2 wt % and 3 wt % nanoparticles in cement mortar led to a positive contribution to strength and resistance to water absorption. Mixture of PP-MK10NC3 indicated the best results for both compressive and flexural strengths at 28 and 90 days. SEM images illustrated that the morphology of cement matrix became more porous with increasing MK content, but the porosity reduced with the inclusion of NC. In addition, it is evident from the SEM images that more cement hydration products adhered onto the surface of fibres, which would improve the fibre–matrix interface. The numerical results obtained by ANFIS and Pegasos were close to the experimental results. The value of R2 obtained for each data set (validate, test and train was higher than 0.90 and the values of mean absolute percentage error (MAPE and the relative root mean squared error (PRMSE were near zero. The ANFIS and Pegasos models can be used to predict the mechanical properties and
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)
1 RESEARCH ARTICLE Neuro-Fuzzy Model of Homocysteine ...
Indian Academy of Sciences (India)
2017-03-10
Mar 10, 2017 ... diseases and higher incidence of vitamin deficiencies in Indians, we ... circulation as peroxynitrite (Antoniades C, et al., 2006); iii) homocysteine was shown to induce damage to endothelium (Pushpakumar S, et al., 2014); iv) elevated ..... 2014 Impact of hyperhomocysteinemia on breast cancer initiation and.
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)
A NEURO FUZZY MODEL FOR THE INVESTIGATION OF ...
African Journals Online (AJOL)
Nigerian Journal of Technology. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives · Journal Home > Vol 36, No 1 (2017) >. Log in or Register to get access to full text downloads. Username, Password, Remember me, or Register · Download this PDF file. The PDF file you selected should ...
Neuro - Fuzzy Analysis for Silicon Carbide Abrasive Grains ...
African Journals Online (AJOL)
Grinding wheels are made of very small, sharp and hard abrasive materials or grits held together by strong porous bond. Abrasive materials are materials of extreme hardness that are used to shape other materials by a grinding or abrading action and they are used either as loose grains, as grinding wheels, or as coatings ...
Adaptive neuro-fuzzy system for malware detection | Sodiya ...
African Journals Online (AJOL)
The implementation of ANFSMD was carried out using Java Programming Language, Interactive Disassembler and Matlab because of their supports for implementation of micro-programs. A total of 20,750 malware programs from VX Heaven public dataset and 15,000 clean files from Filehippo were used for the evaluation.
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.
A Neuro-Fuzzy Multi Swarm FastSLAM Framework
Havangi, R.; Teshnehlab, M.; Nekoui, M. A.
2010-01-01
FastSLAM is a framework for simultaneous localization using a Rao-Blackwellized particle filter. In FastSLAM, particle filter is used for the mobile robot pose (position and orientation) estimation, and an Extended Kalman Filter (EKF) is used for the feature location's estimation. However, FastSLAM degenerates over time. This degeneracy is due to the fact that a particle set estimating the pose of the robot loses its diversity. One of the main reasons for loosing particle diversity in FastSLA...
Cheung, Kristina Alyssa
This project focuses on the characterization of materials from burial offerings and painted decoration in a royal Maya tomb at El Zotz, Guatemala, and their association with mortuary rituals. Archaeological findings included vessels, jade masks, organic materials (wood, cord, and textiles), specular hematite cubes, shells with powdered cinnabar, green (malachite) painted stucco assumed to have decorated the wooden bier where the king was resting, and caches of lip-to-lip Aguila Orange bowls containing human phalanges. This paper describes findings from non-invasive and non-destructive analytical techniques including XRF, VPSEM-EDS, and XRD, emphasizing the potential of these combined technologies in the identification of organic and inorganic markers to infer burial customs. The nature and location of the findings, the evidence of pigment coloration on the bones employing hematite and cinnabar, and the indication of exposure of the bones to high temperatures suggest highly complex, even protracted mortuary practices of Maya elite.
Topuz, E.; van Gestel, C.A.M.
2016-01-01
The usage of Engineered Nanoparticles (ENPs) in consumer products is relatively new and there is a need to conduct environmental risk assessment (ERA) to evaluate their impacts on the environment. However, alternative approaches are required for ERA of ENPs because of the huge gap in data and
Energy Technology Data Exchange (ETDEWEB)
Schlager, H; Schulte, P; Tremmel, H G; Ziereis, H [Deutsche Forschungsanstalt fuer Luft- und Raumfahrt e.V. (DLR), Wessling (Germany). Inst. fuer Physik der Atmosphaere; Arnold, F; Droste-Franke, B; Klemm, M; Schneider, J [Max-Planck-Inst. fuer Kernphysik, Heidelberg (Germany)
1998-12-31
The speciation of NO{sub y} exhaust emissions in the near-field plume of a B747 cruising at 9.2 km was measured in situ using the DLR Falcon research aircraft instrumented with a chemical ionisation mass spectrometer of MPI-K and a chemiluminescence NO detector of DLR. In addition, CO{sub 2} was measured providing a dilution factor for the exhaust species. Observed maximum peak concentrations above background in the plume 60 s after emission were 25.4 ppmv (CO{sub 2}), 184 ppbv (NO), 2.6 ppbv (HNO{sub 2}), and 1.3 ppbv (HNO{sub 3}). The observations were used to infer the initial OH concentration (15.4 ppmv) and NO{sub 2}/NO{sub x} ratio (0.08) at the engine exit by back calculations using a chemistry box model. (author) 20 refs.
Energy Technology Data Exchange (ETDEWEB)
Schlager, H.; Schulte, P.; Tremmel, H.G.; Ziereis, H. [Deutsche Forschungsanstalt fuer Luft- und Raumfahrt e.V. (DLR), Wessling (Germany). Inst. fuer Physik der Atmosphaere; Arnold, F.; Droste-Franke, B.; Klemm, M.; Schneider, J. [Max-Planck-Inst. fuer Kernphysik, Heidelberg (Germany)
1997-12-31
The speciation of NO{sub y} exhaust emissions in the near-field plume of a B747 cruising at 9.2 km was measured in situ using the DLR Falcon research aircraft instrumented with a chemical ionisation mass spectrometer of MPI-K and a chemiluminescence NO detector of DLR. In addition, CO{sub 2} was measured providing a dilution factor for the exhaust species. Observed maximum peak concentrations above background in the plume 60 s after emission were 25.4 ppmv (CO{sub 2}), 184 ppbv (NO), 2.6 ppbv (HNO{sub 2}), and 1.3 ppbv (HNO{sub 3}). The observations were used to infer the initial OH concentration (15.4 ppmv) and NO{sub 2}/NO{sub x} ratio (0.08) at the engine exit by back calculations using a chemistry box model. (author) 20 refs.
Diesel engine NOx emissions control: An advanced method for the O2 evaluation in the intake flow
International Nuclear Information System (INIS)
Mariani, F.; Grimaldi, C.N.; Battistoni, M.
2014-01-01
Highlights: • Prediction of the volumetric oxygen concentration at the intake using ANN and Neuro-Fuzzy approach. • Analysis of exp data acquired from a compression ignition engine in transient operational conditions. • Aging engine: considerations on the on-board training time. • The role of the linear and non-linear transfer function in the ANN model. - Abstract: In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustion engines (ICE) and the pollutant emissions systems. At present, one of the main problems, in the development of diesel engines is represented by the achievement of an increasingly strict control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but increased efficiency in terms of emissions abatement, is necessary in order to determine as best possible the actual oxygen content in the charge at the engine intake manifold. The present work compares the ability of the ANN and Neuro-Fuzzy approach (ANFIS) to predict the volumetric oxygen concentration at the intake, using experimental data acquired on a compression ignition engine in transient operational conditions. In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%). The comparison shows that the ANFIS model produces more accurate solutions in less time, using linear rules that bind the input variables with the output. The linearity of the rules is a
Caticha, Ariel
2011-03-01
In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.
Kroese, A.H.; van der Meulen, E.A.; Poortema, Klaas; Schaafsma, W.
1995-01-01
The making of statistical inferences in distributional form is conceptionally complicated because the epistemic 'probabilities' assigned are mixtures of fact and fiction. In this respect they are essentially different from 'physical' or 'frequency-theoretic' probabilities. The distributional form is
Caticha, Ariel
2010-01-01
In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEn...
Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi
2016-09-01
This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.
Aggelopoulos, Nikolaos C
2015-08-01
Perceptual inference refers to the ability to infer sensory stimuli from predictions that result from internal neural representations built through prior experience. Methods of Bayesian statistical inference and decision theory model cognition adequately by using error sensing either in guiding action or in "generative" models that predict the sensory information. In this framework, perception can be seen as a process qualitatively distinct from sensation, a process of information evaluation using previously acquired and stored representations (memories) that is guided by sensory feedback. The stored representations can be utilised as internal models of sensory stimuli enabling long term associations, for example in operant conditioning. Evidence for perceptual inference is contributed by such phenomena as the cortical co-localisation of object perception with object memory, the response invariance in the responses of some neurons to variations in the stimulus, as well as from situations in which perception can be dissociated from sensation. In the context of perceptual inference, sensory areas of the cerebral cortex that have been facilitated by a priming signal may be regarded as comparators in a closed feedback loop, similar to the better known motor reflexes in the sensorimotor system. The adult cerebral cortex can be regarded as similar to a servomechanism, in using sensory feedback to correct internal models, producing predictions of the outside world on the basis of past experience. Copyright © 2015 Elsevier Ltd. All rights reserved.
Verifying Stability of Dynamic Soft-Computing Systems
Wen, Wu; Napolitano, Marcello; Callahan, John
1997-01-01
Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering, have demonstrated their appropriateness in building intelligent systems that are flexible and robust. Although recent research have shown that certain class of neuro-fuzzy controllers can be proven bounded and stable, they are implementation dependent and difficult to apply to the design and validation process. Many practitioners adopt the trial and error approach for system validation or resort to exhaustive testing using prototypes. In this paper, we describe our on-going research towards establishing necessary theoretic foundation as well as building practical tools for the verification and validation of soft-computing systems. A unified model for general neuro-fuzzy system is adopted. Classic non-linear system control theory and recent results of its applications to neuro-fuzzy systems are incorporated and applied to the unified model. It is hoped that general tools can be developed to help the designer to visualize and manipulate the regions of stability and boundedness, much the same way Bode plots and Root locus plots have helped conventional control design and validation.
Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; SubbaRao; Harish, N.; Lokesha
Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models...
An intelligent clustering based methodology for confusable diseases ...
African Journals Online (AJOL)
Journal of Computer Science and Its Application ... In this paper, an intelligent system driven by fuzzy clustering algorithm and Adaptive Neuro-Fuzzy Inference System for ... Data on patients diagnosed and confirmed by laboratory tests of viral ...
Rohatgi, Vijay K
2003-01-01
Unified treatment of probability and statistics examines and analyzes the relationship between the two fields, exploring inferential issues. Numerous problems, examples, and diagrams--some with solutions--plus clear-cut, highlighted summaries of results. Advanced undergraduate to graduate level. Contents: 1. Introduction. 2. Probability Model. 3. Probability Distributions. 4. Introduction to Statistical Inference. 5. More on Mathematical Expectation. 6. Some Discrete Models. 7. Some Continuous Models. 8. Functions of Random Variables and Random Vectors. 9. Large-Sample Theory. 10. General Meth
Directory of Open Access Journals (Sweden)
A Safrangian
2017-05-01
Full Text Available Introduction Vibrations include a wide range of engineering sciences and discuss from different aspects. One of the aspects is related to various types of engines vibrations, which are often used as power sources in agriculture. The created vibrations can cause lack of comfort and reduce effective work and have bad influence on the health and safety. One of the important parameters of the diesel engine that has the ability to create vibration and knocking is the type of fuel. In this study, the effects of different blends of biodiesel, bioethanol and diesel on the engine vibration were investigated. As a result, a blend of fuels such as synthetic fuel that creates less vibration engine can be identified and introduced. Materials and Methods In this study, canola oil and methanol alcohol with purity of 99.99% and the molar ratio of 6:1 and sodium hydroxide catalyst with 1% by weight of oil were used for biodiesel production. Reactor configurations include: maintaining the temperature at 50 ° C, the reaction time of 5 minutes and the intensity of mixing (8000 rpm, and pump flow, 0.83 liters per minute. A Massey Ferguson (MF 285 tractor with single differential (2WD, built in 2012 at Tractor factory of Iran was used for the experiment. To measure the engine vibration signals, an oscillator with model of VM120 British MONITRAN was used. Vibration signals were measured at three levels of engine speed (2000, 1600, 1000 rpm in three directions (X, Y, Z. The analysis performed by two methods in this study: statistical data analysis and data analysis using Adaptive neuro-fuzzy inference system (ANFIS. Statistical analysis of data: a factorial experiment of 10×3 based on completely randomized design with three replications was used in each direction of X, Y and Z that conducted separately. Data were compiled and analyzed by SPSS 19 software. Ten levels of fuel were including of biodiesel (5, 15 and 25% and bioethanol (2, 4 and 6%, and diesel fuel. Data
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. .
Directory of Open Access Journals (Sweden)
Martín Darío Arango Serna
2012-12-01
Full Text Available En este artículo se desarrolla un modelo de inferencia difusa para la toma de decisiones en condiciones de incertidumbre aplicado al diseño de productos bajo un esquema de ingeniería concurrente. Los requisitos del cliente y los criterios de los diferentes equipos interdisciplinarios para evaluar un diseño en particular son presentados como variables difusas. El modelo aquí desarrollado es aplicado a una empresa de confecciones.In this article a fuzzy inference model is developed for decision making under uncertainty conditions, applied to the design of products under a scheme of concurrent engineering. Customer requirements and criteria of different interdisciplinary teams to evaluate a particular design are presented as fuzzy variables. The model developed is applied to a garment company.
Directory of Open Access Journals (Sweden)
A. R Abdollahnejad Barough
2016-04-01
. Finally, a total amount of the second moment (m2 and matrix vectors of image were selected as features. Features and rules produced from decision tree fed into an Adaptable Neuro-fuzzy Inference System (ANFIS. ANFIS provides a neural network based on Fuzzy Inference System (FIS can produce appropriate output corresponding input patterns. Results and Discussion: The proposed model was trained and tested inside ANFIS Editor of the MATLAB software. 300 images, including closed shell, pithy and empty pistachio were selected for training and testing. This network uses 200 data related to these two features and were trained over 200 courses, the accuracy of the result was 95.8%. 100 image have been used to test network over 40 courses with accuracy 97%. The time for the training and testing steps are 0.73 and 0.31 seconds, respectively, and the time to choose the features and rules was 2.1 seconds. Conclusions: In this study, a model was introduced to sort non- split nuts, blank nuts and filled nuts pistachios. Evaluation of training and testing, shows that the model has the ability to classify different types of nuts with high precision. In the previously proposed methods, merely non-split and split pistachio nuts were sorted and being filled or blank nuts is unrecognizable. Nevertheless, accuracy of the mentioned method is 95.56 percent. As well as, other method sorted non-split and split pistachio nuts with an accuracy of 98% and 85% respectively for training and testing steps. The model proposed in this study is better than the other methods and it is encouraging for the improvement and development of the model.
DEFF Research Database (Denmark)
Andersen, Jesper
2009-01-01
Collateral evolution the problem of updating several library-using programs in response to API changes in the used library. In this dissertation we address the issue of understanding collateral evolutions by automatically inferring a high-level specification of the changes evident in a given set ...... specifications inferred by spdiff in Linux are shown. We find that the inferred specifications concisely capture the actual collateral evolution performed in the examples....
Design and Implementation of Neuro-Fuzzy Controller Using FPGA for Sun Tracking System
Ammar A. Aldair; Adel A. Obed; Ali F. Halihal
2016-01-01
Nowadays, renewable energy is being used increasingly because of the global warming and destruction of the environment. Therefore, the studies are concentrating on gain of maximum power from this energy such as the solar energy. A sun tracker is device which rotates a photovoltaic (PV) panel to the sun to get the maximum power. Disturbances which are originated by passing the clouds are one of great challenges in design of the controller in addition to the losses power due to energy consumpti...
A robust neuro-fuzzy classifier for the detection of cardiomegaly in digital chest radiographies
Directory of Open Access Journals (Sweden)
Fabián Torres-Robles
2014-01-01
Full Text Available Presentamos un nuevo procedimiento que determina de forma automática y fiable la presencia de cardiomegalia en radiografías torácicas. El CTR muestra la relación entre el tamaño del corazón y el tamaño del tórax. El esquema propuesto utiliza un clasificador robusto difuso para encontrar los valores correctos del tamaño del tórax y los límites del corazón derecho e izquierdo para medir el agrandamiento del corazón para detectar cardiomegalia. El método propuesto utiliza operaciones clásicas de morfología para segmentar los pulmones proporcionando baja complejidad computacional y el método difuso propuesto es robusto para encontrar las medidas correctas del CTR proporcionando un cálculo rápido porque las reglas difusas usan operaciones aritméticas elementales para desempeñar una buena detección de cardiomegalia. Finalmente, se mejoran los resultados de clasificación del método difuso propuesto utilizando una red neuronal función de base radial (RBF en términos de precisión, sensibilidad y especificidad.
Edificio project: A neuro-fuzzy approach to building energy management systems
Galata, A.; Bakker, L.G.; Morel, N.; Michel, J.B.; Karki, S.; Joergl, H.P.; Franceschini, A.; Martinez, A.
1998-01-01
It is well known that building installations for indoor climate control, consume a substantial part of the total energy consumption and that at present these installations use much more energy than required due to inadequate settings and poor control and management strategies. European building
Digital modelling of landscape and soil in a mountainous region: A neuro-fuzzy approach
Viloria, Jesús A.; Viloria-Botello, Alvaro; Pineda, María Corina; Valera, Angel
2016-01-01
Research on genetic relationships between soil and landforms has largely improved soil mapping. Recent technological advances have created innovative methods for modelling the spatial soil variation from digital elevation models (DEMs) and remote sensors. This generates new opportunities for the application of geomorphology to soil mapping. This study applied a method based on artificial neural networks and fuzzy clustering to recognize digital classes of land surfaces in a mountainous area in north-central Venezuela. The spatial variation of the fuzzy memberships exposed the areas where each class predominates, while the class centres helped to recognize the topographic attributes and vegetation cover of each class. The obtained classes of terrain revealed the structure of the land surface, which showed regional differences in climate, vegetation, and topography and landscape stability. The land-surface classes were subdivided on the basis of the geological substratum to produce landscape classes that additionally considered the influence of soil parent material. These classes were used as a framework for soil sampling. A redundancy analysis confirmed that changes of landscape classes explained the variation in soil properties (p = 0.01), and a Kruskal-Wallis test showed significant differences (p = 0.01) in clay, hydraulic conductivity, soil organic carbon, base saturation, and exchangeable Ca and Mg between classes. Thus, the produced landscape classes correspond to three-dimensional bodies that differ in soil conditions. Some changes of land-surface classes coincide with abrupt boundaries in the landscape, such as ridges and thalwegs. However, as the model is continuous, it disclosed the remaining variation between those boundaries.
A transfer learning framework for traffic video using neuro-fuzzy ...
Indian Academy of Sciences (India)
P M Ashok Kumar
2017-08-04
Aug 4, 2017 ... The proposed ANFIS-LCTE transfer learning model consists of four steps. (1) Low level visual items .... algorithm clusters object trajectories based on the distance parameters like Euclidean ..... Machine Learning, pp. 113–120.
Interoperable neuro-fuzzy services for emotion-aware ambient intelligence
Acampora, G.; Vitiello, A.
2013-01-01
Ambient Intelligence (AmI) carries out a futuristic vision of living environments which are sensitive and responsive to the presence of people and, by taking care of their desires, intelligently respond to their actions improving their comfort and well-being. Typically, AmI frameworks are based on
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.
Business Planning in the Light of Neuro-fuzzy and Predictive Forecasting
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.
Boundedly rational learning and heterogeneous trading strategies with hybrid neuro-fuzzy models
Bekiros, S.D.
2009-01-01
The present study deals with heterogeneous learning rules in speculative markets where heuristic strategies reflect the rules-of-thumb of boundedly rational investors. The major challenge for "chartists" is the development of new models that would enhance forecasting ability particularly for time
Genetic Neuro-Fuzzy System for the Intelligent Recognition of Stroke ...
African Journals Online (AJOL)
Stroke is a global pandemic, affecting both developed and developing countries. In Nigeria, a steady rise in affected patients is becoming noticeable to all which inspired the development of this research. Stroke is caused by high blood pressure, smoking cigarettes, family history of stroke, high cholesterol, diabetes, obesity, ...
Application of Neuro-Fuzzy to palm oil production process | Odior ...
African Journals Online (AJOL)
Palm oil is an important nutritional food requirement and in order to facilitate the production of palm oil for consumption, the production process of palm oil has been investigated. The basic operations involved in the production of edible palm oil include; purchase, transportation and reception of oil palm bunches; bunch ...
Analyzing Effect of System Inertia on Grid Frequency Forecasting Usnig Two Stage Neuro-Fuzzy System
Chourey, Divyansh R.; Gupta, Himanshu; Kumar, Amit; Kumar, Jitesh; Kumar, Anand; Mishra, Anup
2018-04-01
Frequency forecasting is an important aspect of power system operation. The system frequency varies with load-generation imbalance. Frequency variation depends upon various parameters including system inertia. System inertia determines the rate of fall of frequency after the disturbance in the grid. Though, inertia of the system is not considered while forecasting the frequency of power system during planning and operation. This leads to significant errors in forecasting. In this paper, the effect of inertia on frequency forecasting is analysed for a particular grid system. In this paper, a parameter equivalent to system inertia is introduced. This parameter is used to forecast the frequency of a typical power grid for any instant of time. The system gives appreciable result with reduced error.
User/Tutor Optimal Learning Path in E-Learning Using Comprehensive Neuro-Fuzzy Approach
Fazlollahtabar, Hamed; Mahdavi, Iraj
2009-01-01
Internet evolution has affected all industrial, commercial, and especially learning activities in the new context of e-learning. Due to cost, time, or flexibility e-learning has been adopted by participators as an alternative training method. By development of computer-based devices and new methods of teaching, e-learning has emerged. The…
A transfer learning framework for traffic video using neuro-fuzzy ...
Indian Academy of Sciences (India)
P M Ashok Kumar
2017-08-04
Aug 4, 2017 ... applied for each set of temporal transaction to extract latent sequential topics. (4) ANFIS training is done with the back-propagation gradient descent method. The proposed ANFIS model framework is tested on standard dataset and performance is evaluated in terms of training performance and classification ...
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)
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.
Analysis Of A Neuro-Fuzzy Approach Of Air Pollution: Building A Case Study
Directory of Open Access Journals (Sweden)
Ciprian-Daniel NEAGU
2001-12-01
Full Text Available This work illustrates the necessity of an Artificial Intelligence (AI-based approach of air quality in urban and industrial areas. Some related results of Artificial Neural Networks (ANNs and Fuzzy Logic (FL for environmental data are considered: ANNs are proposed to the problem of short-term predicting of air pollutant concentrations in urban/industrial areas, with a special focus in the south-eastern Romania. The problems of designing a database about air quality in an urban/industrial area are discussed. First results confirm ANNs as an improvement of classical models and show the utility of ANNs in a well built air monitoring center.
A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller
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.
Neuro-fuzzy Classification System for Wireless-Capsule Endoscopic Images
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 ...
Inference in hybrid Bayesian networks
International Nuclear Information System (INIS)
Langseth, Helge; Nielsen, Thomas D.; Rumi, Rafael; Salmeron, Antonio
2009-01-01
Since the 1980s, Bayesian networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability techniques (like fault trees and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (the so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability.
Energy Technology Data Exchange (ETDEWEB)
Petrov, S.
1996-10-01
Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.
Bayesian statistical inference
Directory of Open Access Journals (Sweden)
Bruno De Finetti
2017-04-01
Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.
Geometric statistical inference
International Nuclear Information System (INIS)
Periwal, Vipul
1999-01-01
A reparametrization-covariant formulation of the inverse problem of probability is explicitly solved for finite sample sizes. The inferred distribution is explicitly continuous for finite sample size. A geometric solution of the statistical inference problem in higher dimensions is outlined
Bailer-Jones, Coryn A. L.
2017-04-01
Preface; 1. Probability basics; 2. Estimation and uncertainty; 3. Statistical models and inference; 4. Linear models, least squares, and maximum likelihood; 5. Parameter estimation: single parameter; 6. Parameter estimation: multiple parameters; 7. Approximating distributions; 8. Monte Carlo methods for inference; 9. Parameter estimation: Markov chain Monte Carlo; 10. Frequentist hypothesis testing; 11. Model comparison; 12. Dealing with more complicated problems; References; Index.
Nagao, Makoto
1990-01-01
Knowledge and Inference discusses an important problem for software systems: How do we treat knowledge and ideas on a computer and how do we use inference to solve problems on a computer? The book talks about the problems of knowledge and inference for the purpose of merging artificial intelligence and library science. The book begins by clarifying the concept of """"knowledge"""" from many points of view, followed by a chapter on the current state of library science and the place of artificial intelligence in library science. Subsequent chapters cover central topics in the artificial intellig
Logical inference and evaluation
International Nuclear Information System (INIS)
Perey, F.G.
1981-01-01
Most methodologies of evaluation currently used are based upon the theory of statistical inference. It is generally perceived that this theory is not capable of dealing satisfactorily with what are called systematic errors. Theories of logical inference should be capable of treating all of the information available, including that not involving frequency data. A theory of logical inference is presented as an extension of deductive logic via the concept of plausibility and the application of group theory. Some conclusions, based upon the application of this theory to evaluation of data, are also given
Probability and Statistical Inference
Prosper, Harrison B.
2006-01-01
These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.
On quantum statistical inference
Barndorff-Nielsen, O.E.; Gill, R.D.; Jupp, P.E.
2003-01-01
Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, developments in the theory of quantum measurements have
2018-02-15
expressed a variety of inference techniques on discrete and continuous distributions: exact inference, importance sampling, Metropolis-Hastings (MH...without redoing any math or rewriting any code. And although our main goal is composable reuse, our performance is also good because we can use...control paths. • The Hakaru language can express mixtures of discrete and continuous distributions, but the current disintegration transformation
Introductory statistical inference
Mukhopadhyay, Nitis
2014-01-01
This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques.Beginning with a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables, exponential families of dist
Type Inference with Inequalities
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff
1991-01-01
of (monotonic) inequalities on the types of variables and expressions. A general result about systems of inequalities over semilattices yields a solvable form. We distinguish between deciding typability (the existence of solutions) and type inference (the computation of a minimal solution). In our case, both......Type inference can be phrased as constraint-solving over types. We consider an implicitly typed language equipped with recursive types, multiple inheritance, 1st order parametric polymorphism, and assignments. Type correctness is expressed as satisfiability of a possibly infinite collection...
Watson, Jane
2007-01-01
Inference, or decision making, is seen in curriculum documents as the final step in a statistical investigation. For a formal statistical enquiry this may be associated with sophisticated tests involving probability distributions. For young students without the mathematical background to perform such tests, it is still possible to draw informal…
Hybrid Optical Inference Machines
1991-09-27
with labels. Now, events. a set of facts cal be generated in the dyadic form "u, R 1,2" Eichmann and Caulfield (19] consider the same type of and can...these enceding-schemes. These architectures are-based pri- 19. G. Eichmann and H. J. Caulfield, "Optical Learning (Inference)marily on optical inner
Inference rule and problem solving
Energy Technology Data Exchange (ETDEWEB)
Goto, S
1982-04-01
Intelligent information processing signifies an opportunity of having man's intellectual activity executed on the computer, in which inference, in place of ordinary calculation, is used as the basic operational mechanism for such an information processing. Many inference rules are derived from syllogisms in formal logic. The problem of programming this inference function is referred to as a problem solving. Although logically inference and problem-solving are in close relation, the calculation ability of current computers is on a low level for inferring. For clarifying the relation between inference and computers, nonmonotonic logic has been considered. The paper deals with the above topics. 16 references.
Stochastic processes inference theory
Rao, Malempati M
2014-01-01
This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.
Making Type Inference Practical
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff; Oxhøj, Nicholas; Palsberg, Jens
1992-01-01
We present the implementation of a type inference algorithm for untyped object-oriented programs with inheritance, assignments, and late binding. The algorithm significantly improves our previous one, presented at OOPSLA'91, since it can handle collection classes, such as List, in a useful way. Abo......, the complexity has been dramatically improved, from exponential time to low polynomial time. The implementation uses the techniques of incremental graph construction and constraint template instantiation to avoid representing intermediate results, doing superfluous work, and recomputing type information....... Experiments indicate that the implementation type checks as much as 100 lines pr. second. This results in a mature product, on which a number of tools can be based, for example a safety tool, an image compression tool, a code optimization tool, and an annotation tool. This may make type inference for object...
Directory of Open Access Journals (Sweden)
João Paulo Monteiro
2001-12-01
Full Text Available Russell's The Problems of Philosophy tries to establish a new theory of induction, at the same time that Hume is there accused of an irrational/ scepticism about induction". But a careful analysis of the theory of knowledge explicitly acknowledged by Hume reveals that, contrary to the standard interpretation in the XXth century, possibly influenced by Russell, Hume deals exclusively with causal inference (which he never classifies as "causal induction", although now we are entitled to do so, never with inductive inference in general, mainly generalizations about sensible qualities of objects ( whether, e.g., "all crows are black" or not is not among Hume's concerns. Russell's theories are thus only false alternatives to Hume's, in (1912 or in his (1948.
Causal inference in econometrics
Kreinovich, Vladik; Sriboonchitta, Songsak
2016-01-01
This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.
Active inference and learning.
Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O Doherty, John; Pezzulo, Giovanni
2016-09-01
This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
SDG multiple fault diagnosis by real-time inverse inference
International Nuclear Information System (INIS)
Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng
2005-01-01
In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency
SDG multiple fault diagnosis by real-time inverse inference
Energy Technology Data Exchange (ETDEWEB)
Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng
2005-02-01
In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency.
Directory of Open Access Journals (Sweden)
Diwan Mukti Pambuko
2013-10-01
mengetahui warna permukaan pada posisi tersebut dapat dibuat sebuah sistem yang bisa mengidentifikasi kedalaman laut pada posisi tertentu dari warna pada permukaan laut tersebut. Sistem yang dibangun ini menggunakan data kedalaman laut hasil pengukuran manual dan dipadukan dengan data gambar satelit pada posisi yang sama. Kemudian dilakukan proses learning menggunakan teknik Neuro-Fuzzy dengan metode ANFIS (Adaptive Neuro-Fuzzy Inference System dengan kinerja model identifikasi dapat diketahui dari nilai MAPE (Mean Absolute Percentage Error dan MSE (Mean Square Error. Hasil dari pembuatan model identifikasi, diperoleh sistem yang dapat melakukan identifikasi sangat baik dengan error yang diperoleh pada saat proses pengujian sebesar MAPE 9.0024 % dan MSE 0.0034. Kata kunci: bathymetry, citra satelit, neuro-fuzzy, ANFIS
Learning Convex Inference of Marginals
Domke, Justin
2012-01-01
Graphical models trained using maximum likelihood are a common tool for probabilistic inference of marginal distributions. However, this approach suffers difficulties when either the inference process or the model is approximate. In this paper, the inference process is first defined to be the minimization of a convex function, inspired by free energy approximations. Learning is then done directly in terms of the performance of the inference process at univariate marginal prediction. The main ...
Probabilistic inductive inference: a survey
Ambainis, Andris
2001-01-01
Inductive inference is a recursion-theoretic theory of learning, first developed by E. M. Gold (1967). This paper surveys developments in probabilistic inductive inference. We mainly focus on finite inference of recursive functions, since this simple paradigm has produced the most interesting (and most complex) results.
Multimodel inference and adaptive management
Rehme, S.E.; Powell, L.A.; Allen, Craig R.
2011-01-01
Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.
Schwaibold, M; Schöchlin, J; Bolz, A
2002-01-01
For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths: a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.
Design and analysis of experiments in ANFIS modeling for stock price prediction
Directory of Open Access Journals (Sweden)
Meysam Alizadeh
2011-04-01
Full Text Available At the computational point of view, a fuzzy system has a layered structure, similar to an artificial neural network (ANN of the radial basis function type. ANN learning algorithms can be employed for optimization of parameters in a fuzzy system. This neuro-fuzzy modeling approach has preference to explain solutions over completely black-box models, such as ANN. In this paper, we implement the design of experiment (DOE technique to identify the significant parameters in the design of adaptive neuro-fuzzy inference systems (ANFIS for stock price prediction.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2010-01-01
Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente
Emotional inferences by pragmatics
Iza-Miqueleiz, Mauricio
2017-01-01
It has for long been taken for granted that, along the course of reading a text, world knowledge is often required in order to establish coherent links between sentences (McKoon & Ratcliff 1992, Iza & Ezquerro 2000). The content grasped from a text turns out to be strongly dependent upon the reader’s additional knowledge that allows a coherent interpretation of the text as a whole. The world knowledge directing the inference may be of distinctive nature. Gygax et al. (2007) showed that m...
DEFF Research Database (Denmark)
Andersen, Jesper; Lawall, Julia
2010-01-01
A key issue in maintaining Linux device drivers is the need to keep them up to date with respect to evolutions in Linux internal libraries. Currently, there is little tool support for performing and documenting such changes. In this paper we present a tool, spdiff, that identifies common changes...... developers can use it to extract an abstract representation of the set of changes that others have made. Our experiments on recent changes in Linux show that the inferred generic patches are more concise than the corresponding patches found in commits to the Linux source tree while being safe with respect...
Prediction of the heat transfer rate of a single layer wire-on-tube type heat exchanger using ANFIS
Energy Technology Data Exchange (ETDEWEB)
Hayati, Mohsen [Electrical Engineering Department, Faculty of Engineering, Razi University, Tagh-E-Bostan, Kermanshah 67149 (Iran); Computational Intelligence Research Center, Razi University, Tagh-E-Bostan, Kermanshah 67149 (Iran); Rezaei, Abbas; Seifi, Majid [Electrical Engineering Department, Faculty of Engineering, Razi University, Tagh-E-Bostan, Kermanshah 67149 (Iran)
2009-12-15
In this paper, we applied an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for prediction of the heat transfer rate of the wire-on-tube type heat exchanger. Limited experimental data was used for training and testing ANFIS configuration with the help of hybrid learning algorithm consisting of backpropagation and least-squares estimation. The predicted values are found to be in good agreement with the actual values from the experiments with mean relative error less than 2.55%. Also, we compared the proposed ANFIS model to an ANN approach. Results show that the ANFIS model has more accuracy in comparison to ANN approach. Therefore, we can use ANFIS model to predict the performances of thermal systems in engineering applications, such as modeling heat exchangers for heat transfer analysis. (author)
Kisi, Ozgur; Shiri, Jalal
2012-06-01
Estimating sediment volume carried by a river is an important issue in water resources engineering. This paper compares the accuracy of three different soft computing methods, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP), in estimating daily suspended sediment concentration on rivers by using hydro-meteorological data. The daily rainfall, streamflow and suspended sediment concentration data from Eel River near Dos Rios, at California, USA are used as a case study. The comparison results indicate that the GEP model performs better than the other models in daily suspended sediment concentration estimation for the particular data sets used in this study. Levenberg-Marquardt, conjugate gradient and gradient descent training algorithms were used for the ANN models. Out of three algorithms, the Conjugate gradient algorithm was found to be better than the others.
Soft computing methods for geoidal height transformation
Akyilmaz, O.; Özlüdemir, M. T.; Ayan, T.; Çelik, R. N.
2009-07-01
Soft computing techniques, such as fuzzy logic and artificial neural network (ANN) approaches, have enabled researchers to create precise models for use in many scientific and engineering applications. Applications that can be employed in geodetic studies include the estimation of earth rotation parameters and the determination of mean sea level changes. Another important field of geodesy in which these computing techniques can be applied is geoidal height transformation. We report here our use of a conventional polynomial model, the Adaptive Network-based Fuzzy (or in some publications, Adaptive Neuro-Fuzzy) Inference System (ANFIS), an ANN and a modified ANN approach to approximate geoid heights. These approximation models have been tested on a number of test points. The results obtained through the transformation processes from ellipsoidal heights into local levelling heights have also been compared.
Nitrate leaching from a potato field using adaptive network-based fuzzy inference system
DEFF Research Database (Denmark)
Shekofteh, Hosein; Afyuni, Majid M; Hajabbasi, Mohammad-Ali
2013-01-01
and to maximize nutrient use efficiency and production. Design and operation of a drip fertigation system requires understanding of nutrient leaching behavior in cases of shallow rooted crops such as potatoes which cannot extract nutrient from a lower soil depth. This study deals with neuro-fuzzy modeling......The conventional methods of application of nitrogen fertilizers might be responsible for the increased nitrate concentration in groundwater of areas dominated by irrigated agriculture. Appropriate water and nutrient management strategies are required to minimize groundwater pollution...... of nitrate (NO3) leaching from a potato field under a drip fertigation system. In the first part of the study, a two-dimensional solute transport model was used to simulate nitrate leaching from a sandy soil with varying emitter discharge rates and fertilizer doses. The results from the modeling were used...
Research Article Special Issue
African Journals Online (AJOL)
pc
2018-02-24
Feb 24, 2018 ... Adaptive Neuro-Fuzzy Inference System (ANFIS) is a logic controller .... After training process, the checking data was used to validate the models. .... al. 2011). ANFIS Qe. Qc. TCWR. COP. No. of nodes. 67. 67. 67. 67. No. of.
Sadhana | Indian Academy of Sciences
Indian Academy of Sciences (India)
In this paper, authors investigate the use of adaptive neuro-fuzzy inference system (ANFIS) with extended Kalman filter for fetal ECG extraction from one ECG signal recorded at the abdominal areas of the mother's skin. The abdominal ECG is considered to be composite as it contains both mother's and fetus' ECG signals.
Shirmohammadi Chelan, Bagher; Moradi, Hamidreza; Moosavi, Vahid; Semiromi, Majid Taie; Zeinali, Ali
2013-01-01
Drought is accounted as one of the most natural hazards. Studying on drought is important for designing and managing of water resources systems. This research is carried out to evaluate the ability of Wavelet-ANN and adaptive neuro-fuzzy inference system (ANFIS) techniques for meteorological drought
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....
Functional neuroanatomy of intuitive physical inference.
Fischer, Jason; Mikhael, John G; Tenenbaum, Joshua B; Kanwisher, Nancy
2016-08-23
To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "multiple demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.
Feature Inference Learning and Eyetracking
Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.
2009-01-01
Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…
An Inference Language for Imaging
DEFF Research Database (Denmark)
Pedemonte, Stefano; Catana, Ciprian; Van Leemput, Koen
2014-01-01
We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framewor...
Energy Technology Data Exchange (ETDEWEB)
Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Ahn, Sungsoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of); Shin, Jinwoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of)
2017-05-25
Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM). Since it is computationally intractable, approximate methods have been used to resolve the issue in practice, where meanfield (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments, on complete GMs of relatively small size and on large GM (up-to 300 variables) confirm that the newly proposed algorithms outperform and generalize MF and BP.
Social Inference Through Technology
Oulasvirta, Antti
Awareness cues are computer-mediated, real-time indicators of people’s undertakings, whereabouts, and intentions. Already in the mid-1970 s, UNIX users could use commands such as “finger” and “talk” to find out who was online and to chat. The small icons in instant messaging (IM) applications that indicate coconversants’ presence in the discussion space are the successors of “finger” output. Similar indicators can be found in online communities, media-sharing services, Internet relay chat (IRC), and location-based messaging applications. But presence and availability indicators are only the tip of the iceberg. Technological progress has enabled richer, more accurate, and more intimate indicators. For example, there are mobile services that allow friends to query and follow each other’s locations. Remote monitoring systems developed for health care allow relatives and doctors to assess the wellbeing of homebound patients (see, e.g., Tang and Venables 2000). But users also utilize cues that have not been deliberately designed for this purpose. For example, online gamers pay attention to other characters’ behavior to infer what the other players are like “in real life.” There is a common denominator underlying these examples: shared activities rely on the technology’s representation of the remote person. The other human being is not physically present but present only through a narrow technological channel.
Energy Technology Data Exchange (ETDEWEB)
Malange, Fernando C.V. [Universidade do Estado de Mato Grosso (UEMT), Caceres, MT (Brazil). Dept. de Computacao], E-mail: fmalange@gmail.com; Minussi, Carlos R. [Universidade Estadual Paulista (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], E-mail: minussi@dee.feis.unesp.br
2009-07-01
A methodology for identifying and classifying voltage disturbances (harmonics, voltage sag, etc.) using fuzzy ARTMAP neural networks is presented. It is an ART (adaptive resonance theory) architecture family neural network that presents the stability and plasticity properties, which are fundamental requests for developing a reliable electrical systems with reduced processing time. Stability means a guarantee of good solutions; plasticity allows realize the training without restart the system every time there are new patterns to be stored in a weight matrix of the neural network. The training is realized from the wave forms provided by the acquisition data system, using the wavelets theory to generate the coefficients that constitute the input patterns of the neural network. Results from simulations show that the accuracy index is nearly 100%. (author)
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
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.
International Nuclear Information System (INIS)
Geruzzi, P.
1999-01-01
The 'Foxboro SCADA', former 'Automation Systems Division' of Nuovo Pignone, at the end of eighty years, has been involved in the development of a flexible and powerful Diagnostic System for Rotating Machinery designed and manufactured in other divisions of the Company. This system amalgamates, in a single computer, all the functionality nowadays necessary to correctly manage locally and remotely the Evolutionary Maintenance of rotating machines as well as the relevant plants. It's specially designed to plan preventive and emergency maintenance procedures and to help the maintenance staff/service in preventing the occurrence of failures or severe damage to complete turbo-machinery plant including turbine, compressor and other machines. The system is designed to supervise and to analyze the operating state of one or more turbo- machinery units such as turbo-compressors, turbo-generators and turbo-pumps giving an effective support to plan preventive and breakdown maintenance monitoring the performances of each turbo-group's element and analyzing a large number of thermodynamic and mechanical parameters related to high pressure turbines, low pressure turbines, combustion chambers, axial compressors and load (compressors, generators, and pumps). A brief presentation of the system is provided (author) (ml)
Damayanti, A.; Werdiningsih, I.
2018-03-01
The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.
Optimization methods for logical inference
Chandru, Vijay
2011-01-01
Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though ""solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs."" Presenting powerful, proven optimization techniques for logic in
On principles of inductive inference
Kostecki, Ryszard Paweł
2011-01-01
We propose an intersubjective epistemic approach to foundations of probability theory and statistical inference, based on relative entropy and category theory, and aimed to bypass the mathematical and conceptual problems of existing foundational approaches.
Statistical inference via fiducial methods
Salomé, Diemer
1998-01-01
In this thesis the attention is restricted to inductive reasoning using a mathematical probability model. A statistical procedure prescribes, for every theoretically possible set of data, the inference about the unknown of interest. ... Zie: Summary
Statistical inference for stochastic processes
National Research Council Canada - National Science Library
Basawa, Ishwar V; Prakasa Rao, B. L. S
1980-01-01
The aim of this monograph is to attempt to reduce the gap between theory and applications in the area of stochastic modelling, by directing the interest of future researchers to the inference aspects...
Active inference, communication and hermeneutics.
Friston, Karl J; Frith, Christopher D
2015-07-01
Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Optimal inference with suboptimal models: Addiction and active Bayesian inference
Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl
2015-01-01
When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321
Interactive Instruction in Bayesian Inference
DEFF Research Database (Denmark)
Khan, Azam; Breslav, Simon; Hornbæk, Kasper
2018-01-01
An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These pri......An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction....... These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....
On Maximum Entropy and Inference
Directory of Open Access Journals (Sweden)
Luigi Gresele
2017-11-01
Full Text Available Maximum entropy is a powerful concept that entails a sharp separation between relevant and irrelevant variables. It is typically invoked in inference, once an assumption is made on what the relevant variables are, in order to estimate a model from data, that affords predictions on all other (dependent variables. Conversely, maximum entropy can be invoked to retrieve the relevant variables (sufficient statistics directly from the data, once a model is identified by Bayesian model selection. We explore this approach in the case of spin models with interactions of arbitrary order, and we discuss how relevant interactions can be inferred. In this perspective, the dimensionality of the inference problem is not set by the number of parameters in the model, but by the frequency distribution of the data. We illustrate the method showing its ability to recover the correct model in a few prototype cases and discuss its application on a real dataset.
Fuzzy logic applications to control engineering
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.
Eight challenges in phylodynamic inference
Directory of Open Access Journals (Sweden)
Simon D.W. Frost
2015-03-01
Full Text Available The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.
Problem solving and inference mechanisms
Energy Technology Data Exchange (ETDEWEB)
Furukawa, K; Nakajima, R; Yonezawa, A; Goto, S; Aoyama, A
1982-01-01
The heart of the fifth generation computer will be powerful mechanisms for problem solving and inference. A deduction-oriented language is to be designed, which will form the core of the whole computing system. The language is based on predicate logic with the extended features of structuring facilities, meta structures and relational data base interfaces. Parallel computation mechanisms and specialized hardware architectures are being investigated to make possible efficient realization of the language features. The project includes research into an intelligent programming system, a knowledge representation language and system, and a meta inference system to be built on the core. 30 references.
Behavior Intention Derivation of Android Malware Using Ontology Inference
Directory of Open Access Journals (Sweden)
Jian Jiao
2018-01-01
Full Text Available Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach creates ontology for malware intention to model the semantic relation between behaviors and its objects and automates the process of intention derivation by using SWRL rules transformed from intention model and Jess inference engine. Experiments on 75 typical samples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results are consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system.
An algebra-based method for inferring gene regulatory networks.
Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard
2014-03-26
The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the
Object-Oriented Type Inference
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff; Palsberg, Jens
1991-01-01
We present a new approach to inferring types in untyped object-oriented programs with inheritance, assignments, and late binding. It guarantees that all messages are understood, annotates the program with type information, allows polymorphic methods, and can be used as the basis of an op...
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2009-01-01
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....
Mixed normal inference on multicointegration
Boswijk, H.P.
2009-01-01
Asymptotic likelihood analysis of cointegration in I(2) models, see Johansen (1997, 2006), Boswijk (2000) and Paruolo (2000), has shown that inference on most parameters is mixed normal, implying hypothesis test statistics with an asymptotic 2 null distribution. The asymptotic distribution of the
Statistical inference and Aristotle's Rhetoric.
Macdonald, Ranald R
2004-11-01
Formal logic operates in a closed system where all the information relevant to any conclusion is present, whereas this is not the case when one reasons about events and states of the world. Pollard and Richardson drew attention to the fact that the reasoning behind statistical tests does not lead to logically justifiable conclusions. In this paper statistical inferences are defended not by logic but by the standards of everyday reasoning. Aristotle invented formal logic, but argued that people mostly get at the truth with the aid of enthymemes--incomplete syllogisms which include arguing from examples, analogies and signs. It is proposed that statistical tests work in the same way--in that they are based on examples, invoke the analogy of a model and use the size of the effect under test as a sign that the chance hypothesis is unlikely. Of existing theories of statistical inference only a weak version of Fisher's takes this into account. Aristotle anticipated Fisher by producing an argument of the form that there were too many cases in which an outcome went in a particular direction for that direction to be plausibly attributed to chance. We can therefore conclude that Aristotle would have approved of statistical inference and there is a good reason for calling this form of statistical inference classical.
Using the Weibull distribution reliability, modeling and inference
McCool, John I
2012-01-01
Understand and utilize the latest developments in Weibull inferential methods While the Weibull distribution is widely used in science and engineering, most engineers do not have the necessary statistical training to implement the methodology effectively. Using the Weibull Distribution: Reliability, Modeling, and Inference fills a gap in the current literature on the topic, introducing a self-contained presentation of the probabilistic basis for the methodology while providing powerful techniques for extracting information from data. The author explains the use of the Weibull distribution
Fuel Saving Strategy in Spark Ignition Engine Using Fuzzy Logic Engine Torque Control
Aris Triwiyatno; Sumardi
2012-01-01
In the case of injection gasoline engine, or better known as spark ignition engines, an effort to improve engine performance as well as to reduce fuel consumption is a fairly complex problem. Generally, engine performance improvement efforts will lead to increase in fuel consumption. However, this problem can be solved by implementing engine torque control based on intelligent regulation such as the fuzzy logic inference system. In this study, fuzzy logic engine torque regulation is used to c...
Development of an Intelligent Car Engine Fault Troubleshooting ...
African Journals Online (AJOL)
Development of an Intelligent Car Engine Fault Troubleshooting System (CEFTS) ... and also provides a troubleshooting framework for other researchers to work on. Keywords: ... inference engine, knowledge acquisition, artificial intelligence.
Statistical learning and selective inference.
Taylor, Jonathan; Tibshirani, Robert J
2015-06-23
We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.
Bayesian inference with ecological applications
Link, William A
2009-01-01
This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...
Statistical inference an integrated approach
Migon, Helio S; Louzada, Francisco
2014-01-01
Introduction Information The concept of probability Assessing subjective probabilities An example Linear algebra and probability Notation Outline of the bookElements of Inference Common statistical modelsLikelihood-based functions Bayes theorem Exchangeability Sufficiency and exponential family Parameter elimination Prior Distribution Entirely subjective specification Specification through functional forms Conjugacy with the exponential family Non-informative priors Hierarchical priors Estimation Introduction to decision theoryBayesian point estimation Classical point estimation Empirical Bayes estimation Comparison of estimators Interval estimation Estimation in the Normal model Approximating Methods The general problem of inference Optimization techniquesAsymptotic theory Other analytical approximations Numerical integration methods Simulation methods Hypothesis Testing Introduction Classical hypothesis testingBayesian hypothesis testing Hypothesis testing and confidence intervalsAsymptotic tests Prediction...
Bayesian inference on proportional elections.
Directory of Open Access Journals (Sweden)
Gabriel Hideki Vatanabe Brunello
Full Text Available Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.
Causal inference based on counterfactuals
Directory of Open Access Journals (Sweden)
Höfler M
2005-09-01
Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.
System Support for Forensic Inference
Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan
Digital evidence is playing an increasingly important role in prosecuting crimes. The reasons are manifold: financially lucrative targets are now connected online, systems are so complex that vulnerabilities abound and strong digital identities are being adopted, making audit trails more useful. If the discoveries of forensic analysts are to hold up to scrutiny in court, they must meet the standard for scientific evidence. Software systems are currently developed without consideration of this fact. This paper argues for the development of a formal framework for constructing “digital artifacts” that can serve as proxies for physical evidence; a system so imbued would facilitate sound digital forensic inference. A case study involving a filesystem augmentation that provides transparent support for forensic inference is described.
Probability biases as Bayesian inference
Directory of Open Access Journals (Sweden)
Andre; C. R. Martins
2006-11-01
Full Text Available In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them. Previous results show that the weight functions and the observed violations of coalescing and stochastic dominance can be understood from a Bayesian point of view. We will review those results and see that Bayesian methods should also be used as part of the explanation behind other known biases. That means that, although the observed errors are still errors under the be understood as adaptations to the solution of real life problems. Heuristics that allow fast evaluations and mimic a Bayesian inference would be an evolutionary advantage, since they would give us an efficient way of making decisions. %XX In that sense, it should be no surprise that humans reason with % probability as it has been observed.
Statistical inference on residual life
Jeong, Jong-Hyeon
2014-01-01
This is a monograph on the concept of residual life, which is an alternative summary measure of time-to-event data, or survival data. The mean residual life has been used for many years under the name of life expectancy, so it is a natural concept for summarizing survival or reliability data. It is also more interpretable than the popular hazard function, especially for communications between patients and physicians regarding the efficacy of a new drug in the medical field. This book reviews existing statistical methods to infer the residual life distribution. The review and comparison includes existing inference methods for mean and median, or quantile, residual life analysis through medical data examples. The concept of the residual life is also extended to competing risks analysis. The targeted audience includes biostatisticians, graduate students, and PhD (bio)statisticians. Knowledge in survival analysis at an introductory graduate level is advisable prior to reading this book.
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Statistical inference a short course
Panik, Michael J
2012-01-01
A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causal
On Quantum Statistical Inference, II
Barndorff-Nielsen, O. E.; Gill, R. D.; Jupp, P. E.
2003-01-01
Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, theoretical developments in the theory of quantum measurements have brought the basic mathematical framework for the probability calculations much closer to that of classical probability theory. The present paper reviews this field and proposes and inte...
Nonparametric predictive inference in reliability
International Nuclear Information System (INIS)
Coolen, F.P.A.; Coolen-Schrijner, P.; Yan, K.J.
2002-01-01
We introduce a recently developed statistical approach, called nonparametric predictive inference (NPI), to reliability. Bounds for the survival function for a future observation are presented. We illustrate how NPI can deal with right-censored data, and discuss aspects of competing risks. We present possible applications of NPI for Bernoulli data, and we briefly outline applications of NPI for replacement decisions. The emphasis is on introduction and illustration of NPI in reliability contexts, detailed mathematical justifications are presented elsewhere
Variational inference & deep learning : A new synthesis
Kingma, D.P.
2017-01-01
In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.
Variational inference & deep learning: A new synthesis
Kingma, D.P.
2017-01-01
In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.
Continuous Integrated Invariant Inference, Phase I
National Aeronautics and Space Administration — The proposed project will develop a new technique for invariant inference and embed this and other current invariant inference and checking techniques in an...
Variations on Bayesian Prediction and Inference
2016-05-09
inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle
Adaptive Inference on General Graphical Models
Acar, Umut A.; Ihler, Alexander T.; Mettu, Ramgopal; Sumer, Ozgur
2012-01-01
Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional ...
VINE: A Variational Inference -Based Bayesian Neural Network Engine
2018-01-01
word to be written back to the seed RAM. The results generated by every four LF-updaters are selected sequentially by 4 outputs with different...suitable for hardware implementation. Sequential mini-batch gradient descent (SMBGD) optimization is an update rule we propose to integrate with EASI...the layer computation block can perform forward propagation from at most eight input neurons to at most eight output neurons simultaneously . If a
Higher-order probabilistic perceptrons as Bayesian inference engines
International Nuclear Information System (INIS)
Clark, J.W.; Ristig, M.L.
1994-08-01
This letter makes explicit a structural connection between the Bayes optimal classifier operating on K binary input variables and corresponding two-layer perceptron having normalized output activities and couplings from input to output units of all orders up to K. Given a large and unbiased training set and an effective learning algorithm, such a neural network should be able to learn the statistics of the classification problem and match the a posteriori probabilities given by the Bayes optimal classifier. (author). 18 refs
Dynamic Protocol Reverse Engineering: A Grammatical Inference Approach
2008-03-01
domain-specific languages”. OOPSLA ’05: Companion to the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and...Representation to k-TSS Lan- guage Models”. Computación y Sistemas , 3(4):273–244, 2000. ISSN 1405-5546. 256. Trakhtenbrot, B.A. and Y.M. Barzdin. Finite
Sweller, Naomi; Hayes, Brett K
2010-08-01
Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.