MI-ANFIS: A Multiple Instance Adaptive Neuro-Fuzzy Inference System
2015-08-02
Instance AdaptiveNeuro- Fuzzy Inference System We introduce a novel adaptive neuro- fuzzy architecture based on the framework of Multiple Instance Fuzzy ...Inference. The new architecture called Multiple Instance-ANFIS (MI-ANFIS), is an extension of the standard Adaptive Neuro Fuzzy Inference System (ANFIS... Fuzzy Inference REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR/MONITOR’S ACRONYM(S) ARO 8. PERFORMING
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.
Seizure prediction using adaptive neuro-fuzzy inference system.
Rabbi, Ahmed F; Azinfar, Leila; Fazel-Rezai, Reza
2013-01-01
In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patient's data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. We designed an ANFIS classifier constructed based on these features as input. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. The membership function optimization was conducted based on a hybrid learning algorithm. The proposed method achieved highest sensitivity of 80% with false prediction rate as low as 0.46 per hour.
Diagnosis of rotor fault using neuro-fuzzy inference system | Merabet ...
African Journals Online (AJOL)
Also the calculation of the value of relative energy for each level of signal decomposition using package wavelet, which will be useful as data input of adaptive Neuro-Fuzzy inference system (ANFIS). In this method, fuzzy logic is used to make decisions about the machine state. The adaptive Neuro-Fuzzy inference system is ...
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 DVR Controller Design
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Brahim FERDI
2011-06-01
Full Text Available PI controller is very common in the control of DVRs. However, one disadvantage of this conventional controller is its inability to still working well under a wider range of operating conditions. So, as a solution fuzzy controller is proposed in literature. But, the main problem with the conventional fuzzy controllers is that the parameters associated with the membership functions and the rules depend broadly on the intuition of the experts. To overcome this problem, Adaptive Neuro-Fuzzy Inference System (ANFIS based controller design is proposed. The resulted controller is composed of Sugeno fuzzy controller with two inputs and one output. According to the error and error rate of the control system and the output data, ANFIS generates the appropriate fuzzy controller. The simulation results have proved that the proposed design method gives reliable powerful fuzzy controller with a minimum number of membership functions.
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)
Digital Repository Service at National Institute of Oceanography (India)
Harish, N.; Mandal, S.; Rao, S.; Lokesha
The Adaptive Neuro Fuzzy Inference System (ANFIS) model is constructed using experimental data set to predict the damage level of berm breakwater. Experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory...
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.
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...
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...
Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation.
Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian
2017-07-01
The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial. This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system. The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset. The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069. The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS. Copyright © 2017 Elsevier B.V. All rights reserved.
APLIKASI ADAPTIVE NEURO FUZZY INFERENCE SYSTEM PEMILIHAN SISWA MENGIKUTI LOMBA OLIMPIADE MATEMATIKA
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Hendri Hendri
2016-05-01
Saat ini untuk pemilihan siswa yang mengikuti lomba pada SMAN 10 Bekasi dilakukan berdasarkan musyawarah (rapat dan nilai seleksi lomba. Maka pada penelitian ini, Aplikasi berbasis Adaptive Neuro Fuzzy Inference System (ANFIS digunakan dalam memilih siswa yang mengikuti lomba berdasarkan Standar Operasional Prosedur Olimpiade yang dikeluarkan oleh Direktorat Pembinaan Pendidikan Sekolah Menengah Atas.Pengujian dalam penelitian ini menggunakan beberapa fungsi keanggotaan untuk menghasilkan tingkat dugaan yang paling dekat dengan kondisi riil. Dengan menggunakan metode Backpropagation serta fungsi keanggotaan gaussmf dapat menghasilkan root mean square error 0,15248 serta tingkat kelayakan sistemnya memiliki skor 80.87 sehingga sistem pemilihan siswa yang akan memenuhi standar kualitas. Kata Kunci : Siswa Mengikuti Lomba, Adaptive Neuro Fuzzy Inference System.
A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines.
Sun, Zhan-Li; Au, Kin-Fan; Choi, Tsan-Ming
2007-10-01
This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods.
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)
Directory of Open Access Journals (Sweden)
R Sedghi
2014-09-01
Full Text Available Suitable soil structure is important for crop growth. One of the main characteristics of soil structure is the size of soil aggregates. There are several ways of showing the stability of soil aggregates, among which the determination of the median weight diameter of soil aggregates is the most common method. In this paper, a method based on adaptive neuro fuzzy inference system (ANFIS was used to describe the soil fragmentation for seedbed preparation with combination of primary and secondary tillage implements including subsoiler, moldboard plow and disk harrow. Adaptive neuro fuzzy inference system (ANFIS is a suitable approach to solving non-linear problems. ANFIS is a combination of fuzzy inference system (FIS and an artificial neural network (ANN method and it uses the ability of both models. In this study, the model inputs included “soil moisture content”, “tractor forward speed”and “working depth”. The performance of the model was evaluated using the statistical parameters of root mean square error (RMSE, percentage of relative error (ε, mean absolute error (MAE and the coefficient of determination (R2. These parameters were determined as 0.135, 3.6%, 0.122 and 0.981, respectively. For the evaluation of the ANFIS model, the predicted data using this model were compared to the data of artificial neural network model. The simulation results by ANFIS model showed to be closer to the actual data compared with those made by the artificial neural network model.
Design of uav robust autopilot based on adaptive neuro-fuzzy inference system
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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.
Ja'fari, Ahmad; Kadkhodaie-Ilkhchi, Ali; Sharghi, Yoosef; Ghanavati, Kiarash
2012-02-01
Fractures as the most common and important geological features have a significant share in reservoir fluid flow. Therefore, fracture detection is one of the important steps in fractured reservoir characterization. Different tools and methods are introduced for fracture detection from which formation image logs are considered as the common and effective tools. Due to the economical considerations, image logs are available for a limited number of wells in a hydrocarbon field. In this paper, we suggest a model to estimate fracture density from the conventional well logs using an adaptive neuro-fuzzy inference system. Image logs from two wells of the Asmari formation in one of the SW Iranian oil fields are used to verify the results of the model. Statistical data analysis indicates good correlation between fracture density and well log data including sonic, deep resistivity, neutron porosity and bulk density. The results of this study show that there is good agreement (correlation coefficient of 98%) between the measured and neuro-fuzzy estimated fracture density.
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Mohammad Subhi Al-batah
2014-01-01
Full Text Available To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL and high-grade squamous intraepithelial lesion (HSIL. The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.
UAV Controller Based on Adaptive Neuro-Fuzzy Inference System and PID
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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.
Al-batah, Mohammad Subhi; Isa, Nor Ashidi Mat; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi
2014-01-01
To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.
Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator
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Srinivasan Alavandar
2008-01-01
Full Text Available The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. 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. This paper presents the control of six degrees of freedom robot arm (PUMA Robot using Adaptive Neuro Fuzzy Inference System (ANFIS based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers
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...
Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System.
Hosseini, Monireh Sheikh; Zekri, Maryam
2012-01-01
Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated.
Training Hybrid Neuro-Fuzzy System to Infer Permeability in Wells on Maracaibo Lake, Venezuela
Hurtado, Nuri; Torres, Julio
2014-01-01
The high accuracy on inferrring of rocks properties, such as permeability ($k$), is a very useful study in the analysis of wells. This has led to development and use of empirical equations like Tixier, Timur, among others. In order to improve the inference of permeability we used a hybrid Neuro-Fuzzy System (NFS). The NFS allowed us to infer permeability of well, from data of porosity ($\\phi$) and water saturation ($Sw$). The work was performed with data from wells VCL-1021 (P21) and VCL-950 (P50), Block III, Maracaibo Lake, Venezuela. We evaluated the NFS equations ($k_{P50,i}(\\phi_i,Sw_i)$) with neighboring well data ($P21$), in order to verify the validity of the equations in the area. We have used ANFIS in MatLab.
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
Heddam, Salim
2014-01-01
This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling.
Modelling production uncertainties using the adaptive neuro-fuzzy inference system
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Azizi, Amir
2015-05-01
Full Text Available Production throughput measures the performance and behaviour of a production system. Production throughput modelling is complex because of uncertainties in the production line. This study examined the potential application of the adaptive neuro-fuzzy inference system (ANFIS to modelling the throughput of production under five significant production uncertainties: scrap, setup time, break time, demand, and lead time of manufacturing. The effects of these uncertainties on the production of floor tiles were studied by performing 104 observations on the production uncertainties over 104 weeks, based on a weekly production plan in a tile manufacturing industry. The results of the ANFIS model were compared with the multiple linear regression (MLR model. The results showed that the ANFIS model was capable of forecasting production throughput under uncertainty with higher accuracy than was the MLR model, indicated by an R-squared of 98 per cent.
Adaptive Neuro-fuzzy Inference System as Cache Memory Replacement Policy
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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.
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.
REPLACEMENT SPARE PART INVENTORY MONITORING USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM
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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
DALDABAN, Ferhat; USTKOYUNCU, Nurettin
2010-01-01
In this paper, a new method based on adaptive neuro-fuzzy inference system (ANFIS) to estimate the phase inductance of linear switched reluctance motors (LSRMs) is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid learning algorithm, which combines the back-propagation (BP) algorithm and the least square method (LSM), is used to identify the parameters of ANFIS. The translator position and the p...
DALDABAN, Ferhat; USTKOYUNCU, Nurettin
2010-01-01
In this paper, a new method based on adaptive neuro-fuzzy inference system (ANFIS) to estimate the phase inductance of linear switched reluctance motors (LSRMs) is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid learning algorithm, which combines the back-propagation (BP) algorithm and the least square method (LSM), is used to identify the parameters of ANFIS. The translator position and the phase...
Karami, Alimohammad; Yousefi, Tooraj; Ebrahimi, Saeid; Rezaei, Ehsan; Mahmoudinezhad, Sajjad
2013-06-01
This paper reports the application of the adaptive neuro-fuzzy inference system to model the forced convection heat transfer from v-shaped plate internal surfaces exposed to an air impingement slot jet. The aim of the current study is to consider the effects of the angle of the v-shaped plate (Upphi ) , slot-to-plate spacing ratio (Z/W) and the Reynolds number (Re) variation on the average heat transfer from the v-shaped plate.
Performance Analysis of Adaptive Neuro Fuzzy Inference System Control for MEMS Navigation System
Directory of Open Access Journals (Sweden)
Ling Zhang
2014-01-01
Full Text Available Characterized by small volume, low cost, and low power, MEMS inertial sensors are widely concerned and applied in navigation research, environmental monitoring, military, and so on. Notably in indoor and pedestrian navigation, its easily portable feature seems particularly indispensable and important. However, MEMS inertial sensor has inborn low precision and is impressionable and sometimes goes against accurate navigation or even becomes seriously unstable when working for a period of time and the initial alignment and calibration are invalid. A thought of adaptive neuro fuzzy inference system (ANFIS is relied on, and an assistive control modulated method is presented in this paper, which is newly designed to improve the inertial sensor performance by black box control and inference. The repeatability and long-time tendency of the MEMS sensors are tested and analyzed by ALLAN method. The parameters of ANFIS models are trained using reasonable fuzzy control strategy, with high-precision navigation system for reference as well as MEMS sensor property. The MEMS error nonlinearity is measured and modulated through the peculiarity of the fuzzy control convergence, to enhance the MEMS function and the whole MEMS system property. Performance of the proposed model has been experimentally verified using low-cost MEMS inertial sensors, and the MEMS output error is well compensated. The test results indicate that ANFIS system trained by high-precision navigation system can efficiently provide corrections to MEMS output and meet the requirement on navigation performance.
Preliminary Test of Adaptive Neuro-Fuzzy Inference System Controller for Spacecraft Attitude Control
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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.
Classifying work rate from heart rate measurements using an adaptive neuro-fuzzy inference system.
Kolus, Ahmet; Imbeau, Daniel; Dubé, Philippe-Antoine; Dubeau, Denise
2016-05-01
In a new approach based on adaptive neuro-fuzzy inference systems (ANFIS), field heart rate (HR) measurements were used to classify work rate into four categories: very light, light, moderate, and heavy. Inter-participant variability (physiological and physical differences) was considered. Twenty-eight participants performed Meyer and Flenghi's step-test and a maximal treadmill test, during which heart rate and oxygen consumption (VO2) were measured. Results indicated that heart rate monitoring (HR, HRmax, and HRrest) and body weight are significant variables for classifying work rate. The ANFIS classifier showed superior sensitivity, specificity, and accuracy compared to current practice using established work rate categories based on percent heart rate reserve (%HRR). The ANFIS classifier showed an overall 29.6% difference in classification accuracy and a good balance between sensitivity (90.7%) and specificity (95.2%) on average. With its ease of implementation and variable measurement, the ANFIS classifier shows potential for widespread use by practitioners for work rate assessment. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Modelling Dissolved Pollutants in Krishna River Using Adaptive Neuro Fuzzy Inference Systems
Matli, C. S.; Umamahesh, N. V.
2014-01-01
Water quality models are used to describe the discharge concentration relationships in the river. Number of models exists to simulate the pollutant loads in a river, of which some of them are based on simple cause effect relationships and others on highly sophisticated physical and mathematical approaches that require extensive data inputs. Fuzzy rule based modeling extensively used in other disciplines, is attempted in the present study for modeling water quality with respect of dissolved pollutants in Krishna river flowing in Southern part of India. Adaptive Neuro Fuzzy Inference Systems (ANFIS), a recent development in the area of neuro-computing, based on the concept of fuzzy sets is used to model highly non-linear relationships and are capable of adaptive learning. This paper presents the results of the application of ANFIS for modeling dissolved pollutants in the Krishna River. The application and validation of the models is carried out using water quality and flow data obtained from the monitoring stations on the river. The results indicate that the models are quite successful in simulating the physical processes of the relationships between discharge and concentrations.
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.
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)
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.
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.
A concurrent neuro-fuzzy inference system for screening the ecological risk in rivers.
Ocampo-Duque, William; Juraske, Ronnie; Kumar, Vikas; Nadal, Martí; Domingo, José Luis; Schuhmacher, Marta
2012-05-01
A conceptual model to assess water quality in river basins was developed here. The model was based on ecological risk assessment principles, and incorporated a novel ranking and scoring system, based on self-organizing maps, to account for the likely ecological hazards posed by the presence of chemical substances in freshwater. This approach was used to study the chemical pollution in the Ebro River basin (Spain), whose currently applied environmental indices must be revised in terms of scientific accuracy. Ecological hazard indexes for chemical substances were calculated by pattern recognition of persistence, bioaccumulation, and toxicity properties. A fuzzy inference system was proposed to compute ecological risk points (ERP), which are a combination of the ecological hazard to aquatic sensitive organisms and environmental concentrations. By aggregating ERP, changes in water quality over time were estimated. The proposed concurrent neuro-fuzzy model was applied to a comprehensive dataset of the network controlling the levels of dangerous substances, such as metals, pesticides, and polycyclic aromatic hydrocarbons, in the Ebro river basin. The approach was verified by comparison versus biological monitoring. The results showed that water quality in the Ebro river basin is affected by presence of micro-pollutants. The ERP approach is suitable to analyze overall trends of potential threats to freshwater ecosystems by anticipating the likely impacts from multiple substances, although it does not account for synergies among pollutants. Anyhow, the model produces a convenient indicator to search for pollutant levels of concern.
Diagnosis of renal failure disease using Adaptive Neuro-Fuzzy Inference System.
Akgundogdu, Abdurrahim; Kurt, Serkan; Kilic, Niyazi; Ucan, Osman N; Akalin, Nilgun
2010-12-01
Adaptive Neuro-Fuzzy Inference System (ANFIS) is one of the useful and powerful neural network approaches for the solution of function approximation and pattern recognition problems in the last decades. In this paper, the diagnosis of renal failure disease is investigated using ANFIS approach. Totally the raw data of 112 patients is obtained from Istanbul and Cerrahpasa Medical Faculties of Istanbul University, Turkey. Sixty-four of them are related to renal failures and the rest data belong to healthy persons. In ANFIS model, three rules and Gaussian membership functions are chosen, where rules are determined by the subtractive clustering method. Seven parameters of the patients are considered for the input of the system. These are: Blood Urea Nitrogen (BUN), Creatinine, Uric Acid, Potassium (K), Calcium (Ca), Phosphorus (P) and age. We try to decide whether the patient is ill or not. We have reached 100% success in ANFIS and have better results compared to Support Vector Machine (SVM) and Artificial Neural Networks (ANN).
Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application.
Fernandes, Fabiano C; Rigden, Daniel J; Franco, Octavio L
2012-01-01
Antimicrobial peptides (AMPs) are widely distributed defense molecules and represent a promising alternative for solving the problem of antibiotic resistance. Nevertheless, the experimental time required to screen putative AMPs makes computational simulations based on peptide sequence analysis and/or molecular modeling extremely attractive. Artificial intelligence methods acting as simulation and prediction tools are of great importance in helping to efficiently discover and design novel AMPs. In the present study, state-of-the-art published outcomes using different prediction methods and databases were compared to an adaptive neuro-fuzzy inference system (ANFIS) model. Data from our study showed that ANFIS obtained an accuracy of 96.7% and a Matthew's Correlation Coefficient (MCC) of0.936, which proved it to be an efficient model for pattern recognition in antimicrobial peptide prediction. Furthermore, a lower number of input parameters were needed for the ANFIS model, improving the speed and ease of prediction. In summary, due to the fuzzy nature ofAMP physicochemical properties, the ANFIS approach presented here can provide an efficient solution for screening putative AMP sequences and for exploration of properties characteristic of AMPs.
Human action recognition using meta-cognitive neuro-fuzzy inference system.
Subramanian, K; Suresh, S
2012-12-01
We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort.
Grain classifier with computer vision using adaptive neuro-fuzzy inference system.
Sabanci, Kadir; Toktas, Abdurrahim; Kayabasi, Ahmet
2017-09-01
A computer vision-based classifier using an adaptive neuro-fuzzy inference system (ANFIS) is designed for classifying wheat grains into bread or durum. To train and test the classifier, images of 200 wheat grains (100 for bread and 100 for durum) are taken by a high-resolution camera. Visual feature data of the grains related to dimension (#4), color (#3) and texture (#5) as inputs of the classifier are mainly acquired for each grain using image processing techniques (IPTs). In addition to these main data, nine features are reproduced from the main features to ensure a varied population. Thus four sub-sets including categorized features of reproduced data are constituted to examine their effects on the classification. In order to simplify the classifier, the most effective visual features on the results are investigated. The data sets are compared with each other regarding classification accuracy. A simplified classifier having seven selected features is achieved with the best results. In the testing process, the simplified classifier computes the output with 99.46% accuracy and assorts the wheat grains with 100% accuracy. A system which classifies wheat grains with higher accuracy is designed. The proposed classifier integrated to industrial applications can automatically classify a variety of wheat grains. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.
Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.
Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir
2015-01-01
Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems.
Mythili, A; Sujatha, C M; Srinivasan, S; Ramakrishnan, S
2012-01-01
Spirometry is the most frequently performed clinical test to assess the dynamics of pulmonary function in human subjects. It measures airflow from fully inflated lungs through forced expiratory maneuver and generates large data set. However, these investigations often result in incomplete data sets due to the inability of the children and patients to perform this test. Hence, there is a requirement for prediction of significant parameters from the available incomplete data set. In this work, the results of model based prediction of two such significant parameters, Forced Expiratory Volume in one second (FEV1) and, Forced Expiratory Volume in six seconds (FEV6), are reported. The measured spirometric parameters are given as inputs to the Adaptive Neuro Fuzzy Inference System (ANFIS) which classifies data sets using fuzzy system based multilayer architecture. Triangular, Trapezoidal, Gaussian, Pi and Gbell membership functions are used to train and test the prediction process. The performance of the model is evaluated by computing their prediction error statistics of average value, standard deviation and root mean square. Results show that ANFIS model is capable of predicting FEV1 and FEV6 in both normal and abnormal subjects. Trapezoidal membership function predicted FEV1 with high precision and accuracy using a set of 21 rules. Similar prediction accuracy is observed in FEV6 using Gaussian membership function. Further, it is observed that prediction accuracy is found to be high for normal subjects with better correlation with measured values. It appears that this method is useful in enhancing diagnostic relevance of spirometric investigations in case of children and patients who are not able to perform the test as FEV1 and FEV6 are the useful indices to characterize pulmonary abnormalities.
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.
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.
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.
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
Becerra, Miguel A; Orrego, Diana A; Delgado-Trejos, Edilson
2013-01-01
The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs.
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.
Directory of Open Access Journals (Sweden)
Ulfatun Hani'ah
2016-06-01
Full Text Available Peramalan pemakaian air pada bulan januari 2015 sampai April 2015 dapat dilakukan menggunakan perhitungan matematika dengan bantuan ilmu komputer. Metode yang digunakan adalah Adaptive Neuro Fuzzy Inference System (ANFIS dengan bantuan software MATLAB. Untuk pengujian program, dilakukan percobaan dengan memasukkan variabel klas = 2, maksimum epoh = 100, error = 10-6, rentang nilai learning rate = 0.6 sampai 0.9, dan rentang nilai momentum = 0.6 sampai 0.9. Simpulan yang diperoleh adalah bahwa implementasi metode Adaptive Neuro-Fuzzy Inference System dalam peramalan pemakaian air yang pertama adalah membuat rancangan flowchart, melakukan clustering data menggunakan fuzzy C-Mean, menentukan neuron tiap-tiap lapisan, mencari nilai parameter dengan menggunakan LSE rekursif, lalu penentuan perhitungan error menggunakan sum square error (SSE dan membuat sistem peramalan pemakaian air dengan software MATLAB. Setelah dilakukan percobaan hasil yang menunjukkan SSE paling kecil adalah nilai learning rate 0.9 dan momentum 0.6 dengan SSE 0.0080107. Hasil peramalan pemakaian air pada bulan Januari adalah 3.836.138m3, bulan Februari adalah 3.595.188m3, bulan Maret adalah 3.596.416 m3, dan bulan April adalah 3.776.833 m3.
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.
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
Mahandrio, Irsantyo; Budi, Andriantama; Liong, The Houw; Purqon, Acep
2015-09-01
The growing patterns in cultural and mining sectors are interesting particularly in developed country such as in Indonesia. Here, we investigate the local characteristics of stocks between the sectors of agriculture and mining which si representing two leading companies and two common companies in these sectors. We analyze the prediction by using Adaptive Neuro Fuzzy Inference System (ANFIS). The type of Fuzzy Inference System (FIS) is Sugeno type with Generalized Bell membership function (Gbell). Our results show that ANFIS is a proper method to predicting the stock market with the RMSE : 0.14% for AALI and 0.093% for SGRO representing the agriculture sectors, meanwhile, 0.073% for ANTM and 0.1107% for MDCO representing the mining sectors.
Directory of Open Access Journals (Sweden)
Zhixian Yang
2014-01-01
Full Text Available Background electroencephalography (EEG, recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE and sample entropy (SampEn in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.
Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang
2014-01-01
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.
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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.
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.
Kolus, Ahmet; Dubé, Philippe-Antoine; Imbeau, Daniel; Labib, Richard; Dubeau, Denise
2014-11-01
In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C
2013-03-01
This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies.
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.
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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.
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.
Teimouri, Reza; Sohrabpoor, Hamed
2013-12-01
Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.
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.
Rafiei Nazari, Roshanak; Noorian, Simin; Arabameri, Majid
2017-09-23
There are limitations to the basic knowledge regarding various ways by which packaging components migrate into food as well as ways by which various conditions, elements and molecules related to this phenomenon are analysed. This research aimed to model phthalate migration from polyethylene terephthalate bottles containing non-alcoholic beer by performing adaptive neuro-fuzzy inference system (ANFIS) analysis. The data showed that storage temperature, contact surface and storage period correlates with the rate of migration. Migration of phthalate increases with storage duration gradually and reduces under different temperatures and contact surface. Moreover, increased temperature and storage duration resulted in an increase in migration level ranging from 0.6 μg L(-1) to 2.9 μg L(-1) . In summary, the present study used an ANFIS architecture which consists of three inputs (temperature, surface and storage period), Gauss-bell membership functions for each input variable and one output layer, which represent the migration level. The validation and training models showed an excellent match between the experimental and predicted values of ANFIS. Analysis of the model showed that ANFIS is a powerful tool for predicting phthalate migration from bottles containing non-alcoholic beer. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.
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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.
Chiu, Yung-Chia; Chiang, Chih-Wei; Lee, Tsung-Yu
2017-10-01
The aim of this study is to examine the potential of adaptive neuro fuzzy inference system (ANFIS) to estimate biochemical oxygen demand (BOD). To illustrate the applicability of ANFIS method, the upstream catchment of Feitsui Reservoir in Taiwan is chosen as the case study area. The appropriate input variables used to develop the ANFIS models are determined based on the t-test. The results obtained by ANFIS are compared with those by multiple linear regression (MLR) and artificial neural networks (ANNs). Simulated results show that the identified ANFIS model is superior to the traditional MLR and nonlinear ANNs models in terms of the performance evaluated by the Pearson coefficient of correlation, the root mean square error, the mean absolute percentage, and the mean absolute error. These results indicate that ANFIS models are more suitable than ANNs or MLR models to predict the nonlinear relationship within the variables caused by the complexity of aquatic systems and to produce the best fit of the measured BOD concentrations. ANFIS can be seen as a powerful predictive alternative to traditional water quality modeling techniques and extended to other areas to improve the understanding of river pollution trends.
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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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)
Energy Technology Data Exchange (ETDEWEB)
Khorami, M. Tayebi [Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Poonak, Hesarak Tehran (Iran, Islamic Republic of); Chelgani, S. Chehreh [Surface Science Western, Research Park, University of Western Ontario, London (Canada); Hower, James C. [Center for Applied Energy Research, University of Kentucky, Kexington (United States); Jorjani, E. [Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Poonak, Hesarak Tehran (Iran, Islamic Republic of)
2011-01-01
The results of proximate, ultimate, and petrographic analysis for a wide range of Kentucky coal samples were used to predict Free Swelling Index (FSI) using multivariable regression and Adaptive Neuro Fuzzy Inference System (ANFIS). Three different input sets: (a) moisture, ash, and volatile matter; (b) carbon, hydrogen, nitrogen, oxygen, sulfur, and mineral matter; and (c) group-maceral analysis, mineral matter, moisture, sulfur, and R{sub max} were applied for both methods. Non-linear regression achieved the correlation coefficients (R{sup 2}) of 0.38, 0.49, and 0.70 for input sets (a), (b), and (c), respectively. By using the same input sets, ANFIS predicted FSI with higher R{sup 2} of 0.46, 0.82 and 0.95, respectively. Results show that input set (c) is the best predictor of FSI in both prediction methods, and ANFIS significantly can be used to predict FSI when regression results do not have appropriate accuracy. (author)
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.
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; Nho, Chu Won; Hwang, Keum Taek
2017-10-01
The aim of this study was to develop quantitative structure-activity relationship (QSAR) models for predicting antioxidant activities of phenolic compounds. The bond dissociation energy of O-H bond (BDE) was calculated by semi-empirical quantum chemical methods. As a new parameter for QSAR models, sum of reciprocals of BDE of enol and phenol groups (X BDE ) was calculated. Significant correlations were observed between X BDE and antioxidant activities, and X BDE was introduced as a parameter for developing QSAR models. Linear regression-applied QSAR models and adaptive neuro-fuzzy inference system (ANFIS)-applied QSAR models were developed. QSAR models by both of linear regression and ANFIS achieved high prediction accuracies. Among the developed models, ANFIS-applied models achieved better prediction accuracies than linear regression-applied models. From these results, the proposed parameter of X BDE was confirmed as an appropriate variable for predicting and analysing antioxidant activities of phenolic compounds. Also, the ANFIS could be applied on QSAR models to improve prediction accuracy.
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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.
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.
Energy Technology Data Exchange (ETDEWEB)
Al-Hinti, I.; Sakhrieh, A. [Department of Mechanical Engineering, The Hashemite University, Zarqa 13115 (Jordan); Samhouri, M.; Al-Ghandoor, A. [Department of Industrial Engineering, The Hashemite University, Zarqa 13115 (Jordan)
2009-01-15
This paper uses a neuro-fuzzy interface system (ANFIS) to study the effect of boost pressure on the efficiency, brake mean effective pressure (BMEP), and the brake specific fuel consumption (BSFC) of a single cylinder diesel engine. Experimental data were used as inputs to ANFIS to simulate the engine performance characteristics. The experimental as well as the model results emphasize the role of boost pressure in improving the different engine characteristics. The results show that the ANFIS technique can be used adequately to identify the effect of boost pressure on the different engine characteristics. In addition, different data points that were not used for ANFIS training were used to validate the developed models. The results suggest that ANFIS can be used accurately to predict the effect of boost pressure on the different engine characteristics. (author)
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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
Hong, Haoyuan; Panahi, Mahdi; Shirzadi, Ataollah; Ma, Tianwu; Liu, Junzhi; Zhu, A-Xing; Chen, Wei; Kougias, Ioannis; Kazakis, Nerantzis
2017-10-23
Floods are among Earth's most common natural hazards, and they cause major economic losses and seriously affect peoples' lives and health. This paper addresses the development of a flood susceptibility assessment that uses intelligent techniques and GIS. An adaptive neuro-fuzzy inference system (ANFIS) was coupled with a genetic algorithm and differential evolution for flood spatial modelling. The model considers thirteen hydrologic, morphologic and lithologic parameters for the flood susceptibility assessment, and Hengfeng County in China was chosen for the application of the model due to data availability and the 195 total flood events. The flood locations were randomly divided into two subsets, namely, training (70% of the total) and testing (30%). The Step-wise Weight Assessment Ratio Analysis (SWARA) approach was used to assess the relation between the floods and influencing parameters. Subsequently, two data mining techniques were combined with the ANFIS model, including the ANFIS-Genetic Algorithm and the ANFIS-Differential Evolution, to be used for flood spatial modelling and zonation. The flood susceptibility maps were produced, and their robustness was checked using the Receiver Operating Characteristic (ROC) curve. The results showed that the area under the curve (AUC) for all models was >0.80. The highest AUC value was for the ANFIS-DE model (0.852), followed by ANFIS-GA (0.849). According to the RMSE and MSE methods, the ANFIS-DE hybrid model is more suitable for flood susceptibility mapping in the study area. The proposed method is adaptable and can easily be applied in other sites for flood management and prevention. Copyright © 2017 Elsevier B.V. All rights reserved.
Neuro-fuzzy system for prostate cancer diagnosis.
Benecchi, Luigi
2006-08-01
To develop a neuro-fuzzy system to predict the presence of prostate cancer. Neuro-fuzzy systems harness the power of two paradigms: fuzzy logic and artificial neural networks. We compared the predictive accuracy of our neuro-fuzzy system with that obtained by total prostate-specific antigen (tPSA) and percent free PSA (%fPSA). The data from 1030 men (both outpatients and hospitalized patients) were used. All men had a tPSA level of less than 20 ng/mL. Of the 1030 men, 195 (18.9%) had prostate cancer. A neuro-fuzzy system was developed using the coactive neuro-fuzzy inference system model. The mean area under the receiver operating characteristic curve for the neuro-fuzzy system output was 0.799 +/- 0.029 (95% confidence interval 0.760 to 0.835), for tPSA, it was 0.724 +/- 0.032 (95% confidence interval 0.681 to 0.765), and for %fPSA, 0.766 +/- 0.024 (95% confidence interval 0.725 to 0.804). Furthermore, pairwise comparison of the area under the curves evidenced differences among %fPSA, tPSA, and neuro-fuzzy system's output (tPSA versus neuro-fuzzy system's output, P = 0.008; %fPSA versus neuro-fuzzy system's output, P = 0.032). The comparison at 95% sensitivity showed that the neuro-fuzzy system had the best specificity (31.9%). This study presented a neuro-fuzzy system based on both serum data (tPSA and %fPSA) and clinical data (age) to enhance the performance of tPSA to discriminate prostate cancer. The predictive accuracy of the neuro-fuzzy system was superior to that of tPSA and %fPSA.
DEFF Research Database (Denmark)
Liu, Hui; Loh, Poh Chiang; Blaabjerg, Frede
2015-01-01
by employing wavelet transform under different fault conditions. Then the fuzzy logic rules are automatically trained based on the fuzzified fault features to diagnose the different faults. Neither additional sensor nor the capacitor voltages are needed in the proposed method. The high accuracy, good...... 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...
Buyukbingol, Erdem; Sisman, Arzu; Akyildiz, Murat; Alparslan, Ferda Nur; Adejare, Adeboye
2007-06-15
This paper proposes a new method, Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate physicochemical descriptors of certain chemical compounds for their appropriate biological activities in terms of QSAR models with the aid of artificial neural network (ANN) approach combined with the principle of fuzzy logic. The ANFIS was utilized to predict NMDA (N-methyl-d-Aspartate) receptor binding activities of phencyclidine (PCP) derivatives. A data set of 38 drug-like compounds was coded with 1244 calculated molecular structure descriptors (clustered in 20 data sets) which were obtained from several sources, mainly from Dragon software. Prior to the progress to the ANFIS system, descriptors from the best subsets were selected using unsupervised forward selection (UFS) to eliminate redundancy and multicollinearity followed by fuzzy linear regression algorithm (FLR) which was used for variable selection. ANFIS was applied to train the final descriptors (Mor22m, E3s, R3v+, and R1e+) using a hybrid algorithm consisting of back-propagation and least-square estimation while the optimum number and shape of related functions were obtained through the subtractive clustering algorithm. Comparison of the proposed method with traditional methods, that is, multiple linear regression (MLR) and partial least-square (PLS) was also studied and the results indicated that the ANFIS model obtained from data sets achieved satisfactory accuracy.
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
Azeez, Dhifaf; Ali, Mohd Alauddin Mohd; Gan, Kok Beng; Saiboon, Ismail
2013-01-01
Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in
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
A neuro-fuzzy decision support system for the diagnosis of heart failure.
Akinyokun, Charles O; Obot, Okure U; Uzoka, Faith-Michael E; Andy, John J
2010-01-01
A neuro-fuzzy decision support system is proposed for the diagnosis of heart failure. The system comprises; knowledge base (database, neural networks and fuzzy logic) of both the quantitative and qualitative knowledge of the diagnosis of heart failure, neuro-fuzzy inference engine and decision support engine. The neural networks employ a multi-layers perception back propagation learning process while the fuzzy logic uses the root sum square inference procedure. The neuro-fuzzy inference engine uses a weighted average of the premise and consequent parameters with the fuzzy rules serving as the nodes and the fuzzy sets representing the weights of the nodes. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. An experimental study of the decision support system was carried out using cases of some patients from three hospitals in Nigeria with the assistance of their medical personnel who collected patients' data over a period of six months. The results of the study show that the neuro-fuzzy system provides a highly reliable diagnosis, while the emotional and cognitive filters further refine the diagnosis results by taking care of the contextual elements of medical diagnosis.
Energy Technology Data Exchange (ETDEWEB)
Heidary, Saeed, E-mail: saeedheidary@aut.ac.ir; Setayeshi, Saeed, E-mail: setayesh@aut.ac.ir
2015-01-11
This work presents a simulation based study by Monte Carlo which uses two adaptive neuro-fuzzy inference systems (ANFIS) for cross talk compensation of simultaneous {sup 99m}Tc/{sup 201}Tl dual-radioisotope SPECT imaging. We have compared two neuro-fuzzy systems based on fuzzy c-means (FCM) and subtractive (SUB) clustering. Our approach incorporates eight energy-windows image acquisition from 28 keV to 156 keV and two main photo peaks of {sup 201}Tl (77±10% keV) and {sup 99m}Tc (140±10% keV). The Geant4 application in emission tomography (GATE) is used as a Monte Carlo simulator for three cylindrical and a NURBS Based Cardiac Torso (NCAT) phantom study. Three separate acquisitions including two single-isotopes and one dual isotope were performed in this study. Cross talk and scatter corrected projections are reconstructed by an iterative ordered subsets expectation maximization (OSEM) algorithm which models the non-uniform attenuation in the projection/back-projection. ANFIS-FCM/SUB structures are tuned to create three to sixteen fuzzy rules for modeling the photon cross-talk of the two radioisotopes. Applying seven to nine fuzzy rules leads to a total improvement of the contrast and the bias comparatively. It is found that there is an out performance for the ANFIS-FCM due to its acceleration and accurate results.
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.
National Research Council Canada - National Science Library
Kim, Chan Moon; Parnichkun, Manukid
2017-01-01
.... 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...
Torshabi, Ahmad Esmaili
2014-12-01
In external radiotherapy of dynamic targets such as lung and breast cancers, accurate correlation models are utilized to extract real time tumor position by means of external surrogates in correlation with the internal motion of tumors. In this study, a correlation method based on the neuro-fuzzy model is proposed to correlate the input external motion data with internal tumor motion estimation in real-time mode, due to its robustness in motion tracking. An initial test of the performance of this model was reported in our previous studies. In this work by implementing some modifications it is resulted that ANFIS is still robust to track tumor motion more reliably by reducing the motion estimation error remarkably. After configuring new version of our ANFIS model, its performance was retrospectively tested over ten patients treated with Synchrony Cyberknife system. In order to assess the performance of our model, the predicted tumor motion as model output was compared with respect to the state of the art model. Final analyzed results show that our adaptive neuro-fuzzy model can reduce tumor tracking errors more significantly, as compared with ground truth database and even tumor tracking methods presented in our previous works.
Zarei, Kobra; Atabati, Morteza; Kor, Kamalodin
2014-06-01
A quantitative structure-activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero- to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis.
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)
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.
Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, Wen-Ming; Li, R K; Wang, Tzu-Hao
2012-04-01
Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.
Chau, K T; 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.
Alizadeh, Mahdi; Maghsoudi, Omid Haji; Sharzehi, Kaveh; Reza Hemati, Hamid; Kamali Asl, Alireza; Talebpour, Alireza
2017-09-26
Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more accurate. The purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiate normal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction and classification. Primarily, 32 features incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence metrics were computed. Then, mutual information was used to select features with maximal dependence on the target class and with minimal redundancy between features. Finally, a trained classifier, adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor, healthy and unhealthy classes. Classification accuracy of 94.2% was obtained using the proposed pipeline. Such techniques are valuable for accurate detection characterization and interpretation of endoscopic images.
VLSI design of universal approximator neuro-fuzzy systems
Baturone, I.; Sánchez-Solano, Santiago; Barriga, Angel; Jiménez Fernández, Carlos Jesús; Senhadji, Raouf; López, D. R.
2001-01-01
Neuro-fuzzy systems can theoretically solve any problem since they are universal approximators. Besides, they combine the advantages of the neuro and fuzzy paradigms. This paper describes and compares the different strategies that can be adopted to implement the learning and inference mechanisms involved in a neuro-fuzzy system. CAD tools, most of them integrated into the fuzzy system development environment Xfuzzy 2.0, have been developed to assist the designer in the implementation of neuro...
Neuro-fuzzy controller for active ankle foot orthosis
Directory of Open Access Journals (Sweden)
Rishabh Kochhar
2016-09-01
Full Text Available The ankle foot orthosis (AFO is as an assistive device used in foot disability for gait improvement. The objective of this paper was to design a neuro fuzzy controller for an AFO. Adaptive neuro fuzzy inference system (ANFIS was selected after a detailed study of existing neuro-fuzzy architectures. Data of gait pattern was collected with the help of analog gyro sensors. This data was fed to the ANFIS and a fuzzy rule base was created to complete the neuro-fuzzy system which was used to control the gait pattern. Angular velocity and angle of feet served as inputs to the controller and the output was actuation. The results obtained showed sigmoidal membership functions for the various inputs and outputs due to their close resemblance with the normal human gait. Output of the ANFIS showcased the initial data which was fed to the system; the modified data; changed membership functions and error after training.
A transductive neuro-fuzzy controller: application to a drilling process.
Gajate, Agustín; Haber, Rodolfo E; Vega, Pastora I; Alique, José R
2010-07-01
Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage.
Ghanei, S.; Vafaeenezhad, H.; Kashefi, M.; Eivani, A. R.; Mazinani, M.
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.
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.
Elaziz, Mohamed Abd; Moemen, Yasmine S; Hassanien, Aboul Ella; Xiong, Shengwu
2018-01-24
The global prevalence of hepatitis C Virus (HCV) is approximately 3% and one-fifth of all HCV carriers live in the Middle East, where Egypt has the highest global incidence of HCV infection. Quantitative structure-activity relationship (QSAR) models were used in many applications for predicting the potential effects of chemicals on human health and environment. The adaptive neuro-fuzzy inference system (ANFIS) is one of the most popular regression methods for building a nonlinear QSAR model. However, the quality of ANFIS is influenced by the size of the descriptors, so descriptor selection methods have been proposed, although these methods are affected by slow convergence and high time complexity. To avoid these limitations, the antlion optimizer was used to select relevant descriptors, before constructing a nonlinear QSAR model based on the PIC 50 and these descriptors using ANFIS. In our experiments, 1029 compounds were used, which comprised 579 HCVNS5B inhibitors (PIC 50 ~14). The experimental results showed that the proposed QSAR model obtained acceptable accuracy according to different measures, where [Formula: see text] was 0.952 and 0.923 for the training and testing sets, respectively, using cross-validation, while [Formula: see text] was 0.8822 using leave-one-out (LOO).
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.
Yolmeh, Mahmoud; Habibi Najafi, Mohammad B; Salehi, Fakhreddin
2014-01-01
Annatto is commonly used as a coloring agent in the food industry and has antimicrobial and antioxidant properties. In this study, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the effect of annatto dye on Salmonella enteritidis in mayonnaise. The GA-ANN and ANFIS were fed with 3 inputs of annatto dye concentration (0, 0.1, 0.2 and 0.4%), storage temperature (4 and 25°C) and storage time (1-20 days) for prediction of S. enteritidis population. Both models were trained with experimental data. The results showed that the annatto dye was able to reduce of S. enteritidis and its effect was stronger at 25°C than 4°C. The developed GA-ANN, which included 8 hidden neurons, could predict S. enteritidis population with correlation coefficient of 0.999. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.998). Sensitivity analysis results showed that storage temperature was the most sensitive factor for prediction of S. enteritidis population. Copyright © 2014 Elsevier Ltd. All rights reserved.
Karaman, Safa; Ozturk, Ismet; Yalcin, Hasan; Kayacier, Ahmed; Sagdic, Osman
2012-01-15
Apple pomace, orange peel and potato peel, which have important antioxidative compounds in their structures, are byproducts obtained from fruit or vegetable processing. Use of vegetable extracts is popular and a common technique in the preservation of vegetable oils. Utilization of apple pomace, orange peel and potato peel extracts as natural antioxidant agents in refined sunflower oil during storage in order to reduce or retard oxidation was investigated. All byproduct extracts were added at 3000 ppm to sunflower oil and different nonlinear models were constructed for the estimation of oxidation parameters. Peroxide values of sunflower oil samples containing different natural extracts were found to be lower compared to control sample. Adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN) were used for the construction of models that could predict the oxidation parameters and were compared to multiple linear regression (MLR) for the determination of the best model with high accuracy. It was shown that the ANFIS model with high coefficient of determination (R(2) = 0.999) performed better compared to ANN (R(2) = 0.899) and MLR (R(2) = 0.636) for the prediction of oxidation parameters Incorporation of different natural byproduct extracts into sunflower oil provided an important retardation in oxidation during storage. Effective predictive models were constructed for the estimation of oxidation parameters using ANFIS and ANN modeling techniques. These models can be used to predict oxidative parameter values. Copyright © 2011 Society of Chemical Industry.
Ghaedi, M; Hosaininia, R; Ghaedi, A M; Vafaei, A; Taghizadeh, F
2014-10-15
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 (>1229.55m(2)/g) and low pore size (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 (R(2)) 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. Copyright © 2014 Elsevier B.V. All rights reserved.
Xie, Qiuju; Ni, Ji-Qin; Su, Zhongbin
2017-03-05
Ammonia (NH3) 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. Copyright © 2016 Elsevier B.V. All rights reserved.
Aghajani, Khadijeh; Tayebi, Habib-Allah
2017-01-01
In this study, the Mesoporous material SBA-15 were synthesized and then, the surface was modified by the surfactant Cetyltrimethylammoniumbromide (CTAB). Finally, the obtained adsorbent was used in order to remove Reactive Red 198 (RR 198) from aqueous solution. Transmission electron microscope (TEM), Fourier transform infra-red spectroscopy (FTIR), Thermogravimetric analysis (TGA), X-ray diffraction (XRD), and BET were utilized for the purpose of examining the structural characteristics of obtained adsorbent. Parameters affecting the removal of RR 198 such as pH, the amount of adsorbent, and contact time were investigated at various temperatures and were also optimized. The obtained optimized condition is as follows: pH = 2, time = 60 min and adsorbent dose = 1 g/l. Moreover, a predictive model based on ANFIS for predicting the adsorption amount according to the input variables is presented. The presented model can be used for predicting the adsorption rate based on the input variables include temperature, pH, time, dosage, concentration. The error between actual and approximated output confirm the high accuracy of the proposed model in the prediction process. This fact results in cost reduction because prediction can be done without resorting to costly experimental efforts. SBA-15, CTAB, Reactive Red 198, adsorption study, Adaptive Neuro-Fuzzy Inference systems (ANFIS).
Kolus, Ahmet; Imbeau, Daniel; Dubé, Philippe-Antoine; Dubeau, Denise
2015-09-01
This paper presents a new model based on adaptive neuro-fuzzy inference systems (ANFIS) to predict oxygen consumption (V˙O2) from easily measured variables. The ANFIS prediction model consists of three ANFIS modules for estimating the Flex-HR parameters. Each module was developed based on clustering a training set of data samples relevant to that module and then the ANFIS prediction model was tested against a validation data set. Fifty-eight participants performed the Meyer and Flenghi step-test, during which heart rate (HR) and V˙O2 were measured. Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated V˙O2 in the overall HR range, and separately in different HR ranges. The ANFIS prediction model (MAE = 3 ml kg(-1) min(-1)) demonstrated better performance than Rennie et al.'s (MAE = 7 ml kg(-1) min(-1)) and Keytel et al.'s (MAE = 6 ml kg(-1) min(-1)) models, and comparable performance with the standard Flex-HR method (MAE = 2.3 ml kg(-1) min(-1)) throughout the HR range. The ANFIS model thus provides practitioners with a practical, cost- and time-efficient method for V˙O2 estimation without the need for individual calibration. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Yalcin, Hasan; Ozturk, Ismet; Karaman, Safa; Kisi, Ozgur; Sagdic, Osman; Kayacier, Ahmed
2011-05-01
In this study, natural compounds including gallic acid, ellagic acid, quercetin, β-carotene, and retinol were used as antioxidant agents in order to prevent and decrease oxidation in hazelnut oil. Quercetin showed the strongest antioxidative effect among the antioxidative agents, during storage. The accuracy of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models was studied to estimate the oil samples' peroxide value (PV), free fatty acid (FFA), and iodine values (IV). The root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R(2)) statistics were used to evaluate the models' accuracy. Comparison of the models showed that the ANFIS model performed better than the ANN and multiple linear regressions (MLR) models for estimating the PV, FFA, and IV. The values of R(2) and RMSE were found to be 0.9966 and 2.51, 0.6269 and 88.55, 0.5120 and 101.8 for the ANFIS, ANN, and MLR models for PV in testing period, respectively. The MLR was found to be insufficient for estimating various properties of the oil samples. © 2011 Institute of Food Technologists®
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.
Blanes-Vidal, Victoria; Cantuaria, Manuella Lech; Nadimi, Esmaeil S
2017-04-01
Many epidemiological studies have used proximity to sources as air pollution exposure assessment method. However, proximity measures are not generally good surrogates because of their complex non-linear relationship with exposures. Neuro-fuzzy inference systems (NFIS) can be used to map complex non-linear systems, but its usefulness in exposure assessment has not been extensively explored. We present a novel approach for exposure assessment using NFIS, where the inputs of the model were easily-obtainable proximity measures, and the output was residential exposure to an air pollutant. We applied it to a case-study on NH3 pollution, and compared health effects and exposures estimated from NFIS, with those obtained from emission-dispersion models, and linear and non-linear regression proximity models, using 10-fold cross validation. The agreement between emission-dispersion and NFIS exposures was high (Root-mean-square error (RMSE) =0.275, correlation coefficient (r)=0.91) and resulted in similar health effect estimates. Linear models showed poor performance (RMSE=0.527, r=0.59), while non-linear regression models resulted in heterocedasticity, non-normality and clustered data. NFIS could be a useful tool for estimating individual air pollution exposures in epidemiological studies on large populations, when emission-dispersion data are not available. The tradeoff between simplicity and accuracy needs to be considered. Copyright © 2017 Elsevier Inc. All rights reserved.
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.
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.
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.
Asadollahi-Baboli, M
2012-07-01
The purpose of this study was to develop quantitative structure-activity relationship models for N-benzoylindazole derivatives as inhibitors of human neutrophil elastase. These models were developed with the aid of classification and regression trees (CART) and an adaptive neuro-fuzzy inference system (ANFIS) combined with a shuffling cross-validation technique using interpretable descriptors. More than one hundred meaningful descriptors, representing various structural characteristics for all 51 N-benzoylindazole derivatives in the data set, were calculated and used as the original variables for shuffling CART modelling. Five descriptors of average Wiener index, Kier benzene-likeliness index, subpolarity parameter, average shape profile index of order 2 and folding degree index selected by the shuffling CART technique have been used as inputs of the ANFIS for prediction of inhibition behaviour of N-benzoylindazole derivatives. The results of the developed shuffling CART-ANFIS model compared to other techniques, such as genetic algorithm (GA)-partial least square (PLS)-ANFIS and stepwise multiple linear regression (MLR)-ANFIS, are promising and descriptive. The satisfactory results r2p = 0.845, Q2(LOO) = 0.861, r2(L25%O) = 0.829, RMSE(LOO) = 0.305 and RMSE(L25%O) = 0.336) demonstrate that shuffling CART-ANFIS models present the relationship between human neutrophil elastase inhibitor activity and molecular descriptors, and they yield predictions in excellent agreement with the experimental values.
Ameur, Mourad; Derras, Boumédiène; Zendagui, Djawed
2017-12-01
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.
Kentel, E.; Dogulu, N.
2015-12-01
In Turkey the experience and data required for a hydrological model setup is limited and very often not available. Moreover there are many ungauged catchments where there are also many planned projects aimed at utilization of water resources including development of existing hydropower potential. This situation makes runoff prediction at locations with lack of data and ungauged locations where small hydropower plants, reservoirs, etc. are planned an increasingly significant challenge and concern in the country. Flow duration curves have many practical applications in hydrology and integrated water resources management. Estimation of flood duration curve (FDC) at ungauged locations is essential, particularly for hydropower feasibility studies and selection of the installed capacities. In this study, we test and compare the performances of two methods for estimating FDCs in the Western Black Sea catchment, Turkey: (i) FDC based on Map Correlation Method (MCM) flow estimates. MCM is a recently proposed method (Archfield and Vogel, 2010) which uses geospatial information to estimate flow. Flow measurements of stream gauging stations nearby the ungauged location are the only data requirement for this method. This fact makes MCM very attractive for flow estimation in Turkey, (ii) Adaptive Neuro-Fuzzy Inference System (ANFIS) is a data-driven method which is used to relate FDC to a number of variables representing catchment and climate characteristics. However, it`s ease of implementation makes it very useful for practical purposes. Both methods use easily collectable data and are computationally efficient. Comparison of the results is realized based on two different measures: the root mean squared error (RMSE) and the Nash-Sutcliffe Efficiency (NSE) value. Ref: Archfield, S. A., and R. M. Vogel (2010), Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments, Water Resour. Res., 46, W10513, doi:10.1029/2009WR008481.
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.
Memristive Neuro-Fuzzy System.
Merrikh-Bayat, Farnood; Shouraki, Saeed Bagheri
2013-02-01
In this paper, a novel neuro-fuzzy computing system is proposed where its learning is based on the creation of fuzzy relations by using a new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault tolerant, all synaptic weights in our proposed method are always non-negative, and there is no need to adjust them precisely. Finally, this structure is hierarchically expandable, and it can do fuzzy operations in real time since it is implemented through analog circuits. Simulation results confirm the efficiency and applicability of our neuro-fuzzy computing system. They also indicate that this system can be a good candidate to be used for creating artificial brain.
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.
Neuro-fuzzy model for evaluating the performance of processes ...
Indian Academy of Sciences (India)
In this work an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to model the periodic performance of some multi-input single-output (MISO) processes, namely: brewery operations (case study 1) and soap production (case study 2) processes. Two ANFIS models were developed to model the performance of the ...
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)
Gambús, P L; Jensen, E W; Jospin, M; Borrat, X; Martínez Pallí, G; Fernández-Candil, J; Valencia, J F; Barba, X; Caminal, P; Trocóniz, I F
2011-02-01
The increasing demand for anesthetic procedures in the gastrointestinal endoscopy area has not been followed by a similar increase in the methods to provide and control sedation and analgesia for these patients. In this study, we evaluated different combinations of propofol and remifentanil, administered through a target-controlled infusion system, to estimate the optimal concentrations as well as the best way to control the sedative effects induced by the combinations of drugs in patients undergoing ultrasonographic endoscopy. One hundred twenty patients undergoing ultrasonographic endoscopy were randomized to receive, by means of a target-controlled infusion system, a fixed effect-site concentration of either propofol or remifentanil of 8 different possible concentrations, allowing adjustment of the concentrations of the other drug. Predicted effect-site propofol (C(e)pro) and remifentanil (C(e)remi) concentrations, parameters derived from auditory evoked potential, autoregressive auditory evoked potential index (AAI/2) and electroencephalogram (bispectral index [BIS] and index of consciousness [IoC]) signals, as well as categorical scores of sedation (Ramsay Sedation Scale [RSS] score) in the presence or absence of nociceptive stimulation, were collected, recorded, and analyzed using an Adaptive Neuro Fuzzy Inference System. The models described for the relationship between C(e)pro and C(e)remi versus AAI/2, BIS, and IoC were diagnosed for inaccuracy using median absolute performance error (MDAPE) and median root mean squared error (MDRMSE), and for bias using median performance error (MDPE). The models were validated in a prospective group of 68 new patients receiving different combinations of propofol and remifentanil. The predictive ability (P(k)) of AAI/2, BIS, and IoC with respect to the sedation level, RSS score, was also explored. Data from 110 patients were analyzed in the training group. The resulting estimated models had an MDAPE of 32.87, 12.89, and 8
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.
Wang, Cheng-Hang; Liu, Baw-Jhiune; Wu, Lawrence Shih-Hsin
2012-02-01
Asthma is one of the most common chronic diseases in children. It is caused by complicated coactions between various genetic factors and environmental allergens. The study aims to integrate the concept of implementing adaptive neuro-fuzzy inference system (ANFIS) and classification analysis methods for forecasting the association of asthma susceptibility genes on 3 serum IgE groups. The ANFIS model was trained and tested with data sets obtained from 425 asthmatic subjects and 483 non-asthma subjects from the Taiwanese population. We assessed 13 single-nucleotide polymorphisms (SNPs) in seven well-known asthma susceptibility genes; firstly, the proposed ANFIS model learned to reduce input features from the 13 SNPs. And secondly, the classification will be used to classify the serum IgE groups from the simulated SNPs results. The performance of the ANFIS model, classification accuracies and the results confirmed that the integration of ANFIS and classified analysis has potential in association discovery.
Prediction of conductivity by adaptive neuro-fuzzy model.
Akbarzadeh, S; Arof, A K; Ramesh, S; Khanmirzaei, M H; Nor, R M
2014-01-01
Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity.
Prediction of conductivity by adaptive neuro-fuzzy model.
Directory of Open Access Journals (Sweden)
S Akbarzadeh
Full Text Available Electrochemical impedance spectroscopy (EIS is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity.
Optimization of Neuro-Fuzzy System
Directory of Open Access Journals (Sweden)
M. Sarosa
2007-05-01
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.
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).
A novel Neuro-fuzzy classification technique for data mining
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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.
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.
Kim, Chan Moon; Parnichkun, Manukid
2017-02-01
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.
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.
Khoshbin, Fatemeh; Bonakdari, Hossein; Hamed Ashraf Talesh, Seyed; Ebtehaj, Isa; Zaji, Amir Hossein; Azimi, Hamed
2016-06-01
In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error = 3.362 and root mean square error = 0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron-artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.
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.
A transfer learning framework for traffic video using neuro-fuzzy ...
Indian Academy of Sciences (India)
This paper introduces a novelty in the form of Adaptive Neuro-Fuzzy Inference System-Lossy-Count-based Topic Extraction (ANFIS-LCTE) for classification of anomalies in source and target traffic scenes. The process of transforming the input variables, learning the semantic rules in source scene and transferring the model ...
A Synergistic Effect in the Measurement of Neuro-Fuzzy System
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Gorbachev Sergey
2016-01-01
Full Text Available We consider a new type of hybrid neuro-fuzzy system based on fuzzy and neural computing in hierarchical sequential structure, the total effect exceeds the effect of each component separately. The proposed system can be applied to multi-criteria analysis, automatic classification on signs and obtain evidence-based estimates of the efficiency of scientific and technical solutions and technologies, engineering and robotics. An example of a neuro-fuzzy system measuring the intensity of the emotions of a robot, with the extraction of diagnostic decision rules “If & then”.
ADAPTIVE NEURO-FUZZY COMPUTING TECHNIQUE FOR PRECIPITATION ESTIMATION
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Dalibor Petković
2016-08-01
Full Text Available The paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in precipitation estimation. The monthly precipitation data from 29 synoptic stations in Serbia during 1946-2012 are used as case studies. Even though a number of mathematical functions have been proposed for modeling the precipitation estimation, these models still suffer from the disadvantages such as their being very demanding in terms of calculation time. Artificial neural network (ANN can be used as an alternative to the analytical approach since it offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems and fast calculation. Due to its being a crucial problem, this paper presents a process constructed so as to simulate precipitation with an adaptive neuro-fuzzy inference (ANFIS method. ANFIS is a specific type of the ANN family and shows very good learning and prediction capabilities, which makes it an efficient tool for dealing with encountered uncertainties in any system such as precipitation. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS. This intelligent algorithm is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
Skin cancer recognition by using a neuro-fuzzy system.
Salah, Bareqa; Alshraideh, Mohammad; Beidas, Rasha; Hayajneh, Ferial
2011-02-02
Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) and a fuzzy inference system were used in this study as promising modalities for detection of different types of skin cancer. The accuracy rate of the diagnosis of skin cancer by using the hierarchal neural network was 90.67% while using neuro-fuzzy system yielded a slightly higher rate of accuracy of 91.26% in diagnosis skin cancer type. The sensitivity of NN in diagnosing skin cancer was 95%, while the specificity was 88%. Skin cancer diagnosis by neuro-fuzzy system achieved sensitivity of 98% and a specificity of 89%.
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
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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.
A neuro-fuzzy identification of ECG beats.
Chikh, Mohammed Amine; Ammar, Mohammed; Marouf, Radja
2012-04-01
This paper presents a fuzzy rule based classifier and its application to discriminate premature ventricular contraction (PVC) beats from normals. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to discover the fuzzy rules in order to determine the correct class of a given input beat. The main goal of our approach is to create an interpretable classifier that also provides an acceptable accuracy. The performance of the classifier is tested on MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database. On the test set, we achieved an overall sensitivity and specificity of 97.92% and of 94.52% respectively. Experimental results show that the proposed approach is simple and effective in improving the interpretability of the fuzzy classifier while preserving the model performances at a satisfactory level.
Adaptive Neuro-fuzzy approach in friction identification
Zaiyad Muda @ Ismail, Muhammad
2016-05-01
Friction is known to affect the performance of motion control system, especially in terms of its accuracy. Therefore, a number of techniques or methods have been explored and implemented to alleviate the effects of friction. In this project, the Artificial Intelligent (AI) approach is used to model the friction which will be then used to compensate the friction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is chosen among several other AI methods because of its reliability and capabilities of solving complex computation. ANFIS is a hybrid AI-paradigm that combines the best features of neural network and fuzzy logic. This AI method (ANFIS) is effective for nonlinear system identification and compensation and thus, being used in this project.
Ahmed, Hameed Kaleel; Zulquernain, Mallick
2009-01-01
Ration power plants, to generate power, have become common worldwide. One such one is the steam power plant. In such plants, various moving parts of heavy machines generate a lot of noise. Operators are subjected to high levels of noise. High noise level exposure leads to psychological as well physiological problems; different kinds of ill effects. It results in deteriorated work efficiency, although the exact nature of work performance is still unknown. To predict work efficiency deterioration, neuro-fuzzy tools are being used in research. It has been established that a neuro-fuzzy computing system helps in identification and analysis of fuzzy models. The last decade has seen substantial growth in development of various neuro-fuzzy systems. Among them, adaptive neuro-fuzzy inference system provides a systematic and directed approach for model building and gives the best possible design parameters in minimum possible time. This study aims to develop a neuro-fuzzy model to predict the effects of noise pollution on human work efficiency as a function of noise level, exposure time, and age of the operators doing complex type of task.
Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization
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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.
Balbinot, Alexandre; Favieiro, Gabriela
2013-02-21
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).
A Neuro-Fuzzy System for Characterization of Arm Movements
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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.
Zahra Mohammadi; Mohammad Teshnehlab; Mahdi Aliyari Shoorehdeli
2011-01-01
This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then we use two types of flexible neuro-fuzzy systems as controllers. Basic flexible OR-type neuro-fuzzy...
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)
Aplikasi Neuro Fuzzy Controller Pada Sistem Titrasi Pengolah Limbah Cair
Fatkhurrozi, Bagus
2007-01-01
This research is aimed at planning and measuriang the system of liquid waste processing devide with ply neutral reaction that is controlled by computer based on neuro fuzzy controller, in which the system control is fuzzy logical system than can improve control out put response based on nervous net imitation. In this system, it can be seen that computer has a very important role that is to control the proless of all activities in waste processing. Key ward. PH, Neuro fuzzy controller
Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation
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Auday Al-Mayyahi
2014-11-01
Full Text Available This article proposes an adaptive neuro-fuzzy inference system (ANFIS for solving navigation problems of an autonomous ground vehicle (AGV. The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD; right distance (RD and left distance (LD for the low-level motion control. Two heading controllers deploy the angle difference (AD between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles.
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.
1 RESEARCH ARTICLE Neuro-Fuzzy Model of Homocysteine ...
Indian Academy of Sciences (India)
2017-03-10
Mar 10, 2017 ... homeostasis by SHMT1 C1420T or increased flux of folate towards remethylation due to. TYMS 5'-UTR 28bp tandem repeat or non-vegetarian diet can lower homocysteine levels. Keywords: Homocysteine; Multiple Linear Regression; Neuro-Fuzzy design; diet. Introduction. Homocysteine is a non-dietary ...
Neuro-fuzzy system for chaotic time series forecasting
Masulli, Francesco; Studer, Leonard
1997-10-01
We report on an on-going study to assess potential benefits using soft computing methods in forecasting problems. Our goal is to forecast natural phenomena represented by time series that show chaotic features. We use a neuro-fuzzy system for its ability to adapt to numerical data and for the possibility to input and extract expert knowledge expressed in words. We present results of experiments designed to study how to shape a neuro-fuzzy systems to forecast chaotic time series. Our main conclusions are: (1) The neuro-fuzzy system is able to forecast a synthetic chaotic time series with high accuracy if the number of inputs and the time delay between them are chosen adequately. (2) The Takens-Mane theorem from chaos theory gives a useful lower bound on the minimal number of inputs. (3) The time delay between the inputs can not be set a priori. It has to be tuned for every different times series. (4) The number of fuzzy rules seems related to the size of the learning set and not to the structure of the chaotic dynamical system. We tentatively try to interpret the rules that the neuro-fuzzy system has learned. Finally we discuss the adequacy of the whole set of fuzzy rules to forecast locally the dynamical system.
Machining process influence on the chip form and surface roughness by neuro-fuzzy technique
Anicic, Obrad; Jović, Srđan; Aksić, Danilo; Skulić, Aleksandar; Nedić, Bogdan
2017-04-01
The main aim of the study was to analyze the influence of six machining parameters on the chip shape formation and surface roughness as well during turning of Steel 30CrNiMo8. Three components of cutting forces were used as inputs together with cutting speed, feed rate, and depth of cut. It is crucial for the engineers to use optimal machining parameters to get the best results or to high control of the machining process. Therefore, there is need to find the machining parameters for the optimal procedure of the machining process. Adaptive neuro-fuzzy inference system (ANFIS) was used to estimate the inputs influence on the chip shape formation and surface roughness. According to the results, the cutting force in direction of the depth of cut has the highest influence on the chip form. The testing error for the cutting force in direction of the depth of cut has testing error 0.2562. This cutting force determines the depth of cut. According to the results, the depth of cut has the highest influence on the surface roughness. Also the depth of cut has the highest influence on the surface roughness. The testing error for the cutting force in direction of the depth of cut has testing error 5.2753. Generally the depth of cut and the cutting force which provides the depth of cut are the most dominant factors for chip forms and surface roughness. Any small changes in depth of cut or in cutting force which provide the depth of cut could drastically affect the chip form or surface roughness of the working material.
A system-on-chip development of a neuro-fuzzy embedded agent for ambient-intelligence environments.
del Campo, Inés; Basterretxea, Koldo; Echanobe, Javier; Bosque, Guillermo; Doctor, Faiyaz
2012-04-01
This paper presents the development of a neuro-fuzzy agent for ambient-intelligence environments. The agent has been implemented as a system-on-chip (SoC) on a reconfigurable device, i.e., a field-programmable gate array. It is a hardware/software (HW/SW) architecture developed around a MicroBlaze processor (SW partition) and a set of parallel intellectual property cores for neuro-fuzzy modeling (HW partition). The SoC is an autonomous electronic device able to perform real-time control of the environment in a personalized and adaptive way, anticipating the desires and needs of its inhabitants. The scheme used to model the intelligent agent is a particular class of an adaptive neuro-fuzzy inference system with piecewise multilinear behavior. The main characteristics of our model are computational efficiency, scalability, and universal approximation capability. Several online experiments have been performed with data obtained in a real ubiquitous computing environment test bed. Results obtained show that the SoC is able to provide high-performance control and adaptation in a life-long mode while retaining the modeling capabilities of similar agent-based approaches implemented on larger computing machines.
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.
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
Verma, Akhilesh K.; Chaki, Soumi; Routray, Aurobinda; Mohanty, William K.; Jenamani, Mamata
2014-12-01
In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root
Favieiro, Gabriela W; Balbinot, Alexandre
2011-01-01
The myoelectric signal is a sign of control of the human body that contains the information of the user's intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86-98% for three subjects.
PSO based neuro fuzzy sliding mode control for a robot manipulator
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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.
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.
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
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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
Monitoring the depth of anesthesia using a new adaptive neuro-fuzzy system.
Shalbaf, Ahmad; Saffar, Mohsen; Sleigh, Jamie W; Shalbaf, Reza
2017-05-29
Accurate and noninvasive monitoring of the depth of anesthesia (DoA) is highly desirable. Since the anesthetic drugs act mainly on the central nervous system, the analysis of brain activity using electroencephalogram (EEG) is very useful. This paper proposes a novel automated method for assessing the DoA using EEG. Firstly, 11 features including spectral, fractal and entropy are extracted from EEG signal and then, by applying an algorithm according to exhaustive search of all subsets of features, a combination of the best features (Beta-index, sample entropy, shannon permutation entropy and detrended fluctuation analysis) is selected. Accordingly, we feed these extracted features to a new neuro-fuzzy classification algorithm, Adaptive Neuro-Fuzzy Inference System with Linguistic Hedges (ANFIS-LH). This structure can successfully model systems with nonlinear relationships between input and output, and also classify overlapped classes accurately. ANFIS-LH, which is based on modified classical fuzzy rules, reduces the effects of the insignificant features in input space; which causes overlapping and modifies the output layer structure. The presented method classifies EEG data into awake, light, general and deep states during anesthesia with sevoflurane in 17 patients. Its accuracy is 92%, and compared to a commercial monitoring system (RE index) successfully. Moreover, this method reaches the classification accuracy of 93% to categorize EEG signal to awake and general anesthesia states by another database of propofol and volatile anesthesia in 50 patients. To sum up, this method is potentially applicable to a new real time monitoring system to help the anesthesiologist for continuous assessment of DoA quickly and accurately.
NEURO-FUZZY MODELLING OF BLENDING PROCESS IN CEMENT PLANT
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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)
An indispensable element of any wireless communication system is the antenna. microstrip patch antenna (MPA) is well suited for wireless communication due to its light weight, low volume and low profile planar configuration which can be easily conformed to the host surface. In this paper, an adaptive neuro‐fuzzy ...
Lung Nodule Detection in CT Images using Neuro Fuzzy Classifier
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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
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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.
MODELLING AND FORECAST OF CHARCOAL PRICES USING A NEURO-FUZZY SYSTEM
National Research Council Canada - National Science Library
Carlos Alberto Araújo Júnior; Liniker Fernandes da Silva; Marcio Lopes da Silva; Helio Garcia Leite; Erlon Barbosa Valdetaro; Danilo Barros Donato; Renato Vinícius Oliveira Castro
2016-01-01
Using a monthly time series of charcoal prices in Minas Gerais from January 2000 to September 2014, this study aimed to evaluate the use of neuro-fuzzy system to model the series and forecasting prices...
Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.
Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla
2014-12-01
This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.
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Mojtaba Hedayati Marzbali
2017-10-01
Full Text Available This study investigates the use of synthesized mesoporous carbon in the fixed bed adsorption, as a promising process, to eliminate tetracycline from wastewater. In order to study the adsorptive capability of adsorbent, particles were embedded in a laboratory-scale Pyrex glass tube. An increase in initial concentration and decrease in bed height and flow rate led to the higher adsorption capacity. The highest bed capacity of 76.97Â mgÂ gâ1 was obtained using 4Â cm bed depth, 4Â mLÂ minâ1 and 50Â mgÂ Lâ1 influent concentration. The initial part of breakthrough curve perfectly matched the AdamsâBohart model at all experimental conditions. However, it was anticipated that YoonâNelson model could predict the whole curve acceptably, the results showed an inaccurate fitting. Therefore, the adaptive neuro-fuzzy inference system (ANFIS was used to predict the breakthrough curve using data series of adsorption experiments. This model indicated a good statistical prediction in terms of relative errors. Keywords: Apricot shell, Tetracycline, Column adsorption, Machine learning, Neuro-fuzzy
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Vipan K Sohpal
2014-06-01
Full Text Available Transesterification of Jatropha curcus for biodiesel production is a kinetic control process, which is complex in nature and controlled by temperature, the molar ratio, mixing intensity and catalyst process parameters. A precise choice of catalyst is required to improve the rate of transesterification and to simulate the kinetic study in a batch reactor. The present paper uses an Adaptive Neuro-Fuzzy Inference System (ANFIS approach to model and simulate the butyl ester production using alkaline catalyst (NaOH. The amounts of catalyst and time for reaction have been used as the model’s input parameters. The model is a combination of fuzzy inference and artificial neural network, including a set of fuzzy rules which have been developed directly from experimental data. The proposed modeling approach has been verified by comparing the expected results with the practical results which were observed and obtained through a batch reactor operation. The application of the ANFIS test shows which amount of catalyst predicted by the proposed model is suitable and in compliance with the experimental values at 0.5% level of significance.
Peramalan Beban Listrik Harian dengan Metode Adaptive Neuro Fuzzy Inferrence System
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I G.M.W. Meindra Sidemen
2014-06-01
Full Text Available Peningkatan kebutuhan tenaga listrik di Indonesia khususnya di Bali terjadi seiring dengan meningkatnya kegiatan ekonomi dan kesejahteraan masyarakat. Peningkatan kebutuhan tenaga listrik memerlukan suatu perencanaan sistem tenaga listrik yang tepat. Perencanaan yang baik bisa dilakukan melalui peramalan yang tepat untuk kebutuhan beban listrik. Peramalan berdasarkan rentang waktu dikategorikan menjadi peramalan jangka pendek, jangka menengah dan jangka panjang. Salah satu metode dalam sistem cerdas yang dapat digunakan untuk peramalan beban adalah metode adaptive neuro fuzzy inference sistem (ANFIS. Pada penelitian ini, metode tersebut digunakan untuk peramalan beban listrik jangka pendek atau harian. Data yang dipergunakan untuk pembelajaran pada peramalan ini adalah data sebenarnya (actual data yang diambil dari PT.PLN (Persero Area Pengatur Distribusi Bali, mulai dari bulan Juni 2011 sampai dengan Agustus 2011 dan Desember 2012 sampai dengan Februari 2013. Pembangunan Model ANFIS menggunakan program Matlab. Fuzzy Inference System yang digunakan adalah tipe Takagi-Sugeno orde satu, dengan membership function yaitu time (jam, data histori beban listrik sebelumnya, kondisi hari sebelumnya (hari kerja atau hari libur dan kondisi hari ini (hari kerja atau hari libur masing-masing berjumlah 7, 10, 2 dan 2 dengan tipe fungsi keanggotan gbellmf. Penelitian ini menghasilkan nilai MAPE terbesar yaitu 9,92% yang terjadi pada bulan Juni 2011 yang nilainya lebih baik dibandingkan dengan metode JST backpropagation. Jadi metode peramalan beban listrik harian menggunakan metode ANFIS cukup akurat.
An adaptive neuro fuzzy model for estimating the reliability of component-based software systems
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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
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.
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.
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.
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)
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
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.
Directory of Open Access Journals (Sweden)
Pipit Wahyu Nugroho
2014-04-01
Full Text Available The main function of a vehicle suspension system is to isolate the vehicle body from external excitation in order to improve passenger comfort and road holding and to stabilise its movement. This paper considers the implementation of an adaptive neuro fuzzy inference system (ANFIS with a fuzzy hybrid control technique to control a quarter vehicle suspension system with a semiactive magneto rheological (MR damper. A quarter car suspension model is set up with an MR damper and a semiactive controller consisting of a fuzzy hybrid skyhook-groundhook controller and an ANFIS model is also designed. The fuzzy hybrid controller is used to generate the desired control force, and the ANFIS is designed to model the inverse dynamics of MR damper in order to obtain a desired current. Finally, numerical simulations of the semiactive suspensions with the ANFIS-hybrid controller, the traditional hybrid controller, and passive suspension are compared. The results of simulations show that the proposed ANFIS-hybrid controller provides better isolation performance than the other controllers.
Bayesian Regression and Neuro-Fuzzy Methods Reliability Assessment for Estimating Streamflow
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Yaseen A. Hamaamin
2016-07-01
Full Text Available Accurate and efficient estimation of streamflow in a watershed’s tributaries is prerequisite parameter for viable water resources management. This study couples process-driven and data-driven methods of streamflow forecasting as a more efficient and cost-effective approach to water resources planning and management. Two data-driven methods, Bayesian regression and adaptive neuro-fuzzy inference system (ANFIS, were tested separately as a faster alternative to a calibrated and validated Soil and Water Assessment Tool (SWAT model to predict streamflow in the Saginaw River Watershed of Michigan. For the data-driven modeling process, four structures were assumed and tested: general, temporal, spatial, and spatiotemporal. Results showed that both Bayesian regression and ANFIS can replicate global (watershed and local (subbasin results similar to a calibrated SWAT model. At the global level, Bayesian regression and ANFIS model performance were satisfactory based on Nash-Sutcliffe efficiencies of 0.99 and 0.97, respectively. At the subbasin level, Bayesian regression and ANFIS models were satisfactory for 155 and 151 subbasins out of 155 subbasins, respectively. Overall, the most accurate method was a spatiotemporal Bayesian regression model that outperformed other models at global and local scales. However, all ANFIS models performed satisfactory at both scales.
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.
Development of quantum-based adaptive neuro-fuzzy networks.
Kim, Sung-Suk; Kwak, Keun-Chang
2010-02-01
In this study, we are concerned with a method for constructing quantum-based adaptive neuro-fuzzy networks (QANFNs) with a Takagi-Sugeno-Kang (TSK) fuzzy type based on the fuzzy granulation from a given input-output data set. For this purpose, we developed a systematic approach in producing automatic fuzzy rules based on fuzzy subtractive quantum clustering. This clustering technique is not only an extension of ideas inherent to scale-space and support-vector clustering but also represents an effective prototype that exhibits certain characteristics of the target system to be modeled from the fuzzy subtractive method. Furthermore, we developed linear-regression QANFN (LR-QANFN) as an incremental model to deal with localized nonlinearities of the system, so that all modeling discrepancies can be compensated. After adopting the construction of the linear regression as the first global model, we refined it through a series of local fuzzy if-then rules in order to capture the remaining localized characteristics. The experimental results revealed that the proposed QANFN and LR-QANFN yielded a better performance in comparison with radial basis function networks and the linguistic model obtained in previous literature for an automobile mile-per-gallon prediction, Boston Housing data, and a coagulant dosing process in a water purification plant.
Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling
Chiu, Stephen
1996-01-01
Everyday experience in building and repairing things around the home have taught us the importance of using the right tool for the right job. Although we tend to think of a 'job' in broad terms, such as 'build a bookcase,' we understand well that the 'right job' associated with each 'right tool' is typically a narrowly bounded subtask, such as 'tighten the screws.' Unfortunately, we often lose sight of this principle when solving engineering problems; we treat a broadly defined problem, such as controlling or modeling a system, as a narrow one that has a single 'right tool' (e.g., linear analysis, fuzzy logic, neural network). We need to recognize that a typical real-world problem contains a number of different sub-problems, and that a truly optimal solution (the best combination of cost, performance and feature) is obtained by applying the right tool to the right sub-problem. Here I share some of my perspectives on what constitutes the 'right job' for fuzzy control and describe recent advances in neuro-fuzzy modeling to illustrate and to motivate the synergistic use of different tools.
Petchinathan,G.; Valarmathi,K.; Devaraj,D.; Radhakrishnan,T. K.
2014-01-01
This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro-fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a G...
A Neuro-Fuzzy Approach in the Classification of Students' Academic Performance
2013-01-01
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. PMID:24302928
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.
A neuro-fuzzy approach in the classification of students' academic performance.
Do, Quang Hung; Chen, Jeng-Fung
2013-01-01
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.
Classification of EMG signals using neuro-fuzzy system and diagnosis of neuromuscular diseases.
Koçer, Sabri
2010-06-01
This work investigates the performance of neuro-fuzzy system for analyzing and classifying EMG signals recorded from normal, neuropathy, and myopathy subjects. EMG signals were obtained from 177 subjects, 60 of them had suffered from neuropathy disorder, 60 of them had suffered from myopathy disorder, and rest of them had been normal. Coefficients that were obtained from the EMG signals using Autoregressive (AR) analysis was applied to neuro-fuzzy system. The classification performance of the feature sets was investigated for three classes.
A neuro-fuzzy technique for fault diagnosis and its application to rotating machinery
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Zio, Enrico [Department of Nuclear Engineering, Polytechnic of Milan, Via Ponzio 34/3, 20133 Milano (Italy)], E-mail: enrico.zio@polimi.it; Gola, Giulio [Department of Nuclear Engineering, Polytechnic of Milan, Via Ponzio 34/3, 20133 Milano (Italy)
2009-01-15
Malfunctions in machinery are often sources of reduced productivity and increased maintenance costs in various industrial applications. For this reason, machine condition monitoring is being pursued to recognise incipient faults. In this paper, the fault diagnostic problem is tackled within a neuro-fuzzy approach to pattern classification. Besides the primary purpose of a high rate of correct classification, the proposed neuro-fuzzy approach also aims at obtaining an easily interpretable classification model. The efficiency of the approach is verified with respect to a literature problem and then applied to a case of motor bearing fault classification.
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.
Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petković, Dalibor; Mat Kiah, Miss Laiha
2015-01-01
This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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.
Stieler, Florian; Yan, Hui; Lohr, Frank; Wenz, Frederik; Yin, Fang-Fang
2009-01-01
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 study demonstrated a
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.
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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)
Stabilization of an inverted pendulum system via an SIRM neuro-fuzzy controller
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Kulworawanichpong, T.
2005-01-01
Full Text Available This article presents a new neuro-fuzzy controller to stabilize an inverted pendulum system. The proposed controller consists of the Single Input Rule Modules (SIRMs, the artificial neural network (ANN and the dynamic importance degrees (DIDs. It simultaneously controls both the angle of the pendulum and the position of the cart. The learning of the ANN results in the DIDs. The proposed controller has a simple structure that can decrease the number of fuzzy rules. The simulation results show that the proposed neurofuzzy controller has an ability to stabilize a wide range of the inverted pendulum system within a short periodof time. Moreover, the comparisons of the simulation results between the proposed neuro-fuzzy controller and the SIRMs fuzzy controller are revealed in this article.
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.
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.
Marichal, N.; Tomas-Rodriguez, M.; Hernandez, A.; Castillo, S; Campoy, P.
2014-01-01
In this paper, an intelligent control approach based on neuro-fuzzy systems performance is presented, with the objective of counteracting the vibrations that affect the low-cost vision platform onboard an unmanned aerial system of rotating nature. A scaled dynamical model of a helicopter is used to simulate vibrations on its fuselage. The impact of these vibrations on the low-cost vision system will be assessed and an intelligent control approach will be derived in order to reduce its detrime...
Adaptive Neuro-Fuzzy Controller Experimental Design for DC Motor Connected to Unbalanced Load
Reza Nejati; Rahmat Hooshamnd
2007-01-01
In two recent decades, fuzzy controllers have been used in controlling different systems successfully. In this article, a new method is given for controlling of permanent magnetic DC motor connected to unbalanced load. Imbalance of load leads to machine vibrations, fluctuation of power, making exhaustion in machine shaft, and equipment depreciation. In this article neuro-fuzzy controllers are used for controlling unbalanced load. Because of non-linear nature of load and machine, machine fluct...
Prediction of autistic disorder using neuro fuzzy system by applying ANN technique.
Arthi, K; Tamilarasi, A
2008-11-01
The major challenge in medical field is to diagnose disorder rather than a disease. In this paper, a neuro fuzzy based model is designed for identification or diagnosis of autism. The problematic areas are gathered from every individual and the related linguistic inputs are converted into fuzzy input values which are in turn given as input to feed forward multilayer neural network. The network is trained using back propagation training algorithm and tested for its performance with the expertise.
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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.
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Neeraj Kumar Goyal
2007-01-01
Full Text Available Objective: To predict biochemical failure in localized prostate cancer after radical prostatectomy using preoperative variables. Materials and Methods: Twenty-six patients of early carcinoma of prostate underwent open retropubic radical prostatectomy from June 2002 to June 2006. Preoperative variables included age, family history, digital rectal examination, serum prostatic specific antigen (S. PSA, prostate biopsy Gleason score, MRI of pelvis variables like periprostatic extension, seminal vesical invasion, weight of gland and pathological stage. With application of neuro-fuzzy, these variables were fed into system as input and output, that is S. PSA at six months (predicted value was calculated. Neuro-fuzzy system is a system to combine fuzzy system with learning techniques derived from neural networks. Here, we applied Takagi Sugeno Kang model (TSK due to its close solution to our aim. All the patients were followed up for a minimum of six months. At six month S. PSA of all patients was done (observed value. Predicted and observed values were compared. Result: Predicted and observed values were plotted on 1:1 slop line. Coefficient of correlation was 0.9935. Conclusion: Coefficient of correlation is close to one. It indicates that the neuro-fuzzy is accurate in predicting biochemical failure in localized carcinoma of prostate after radical prostatectomy.
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
Wang, Yu; Winters, Jack M
2005-06-28
Intelligent management of wearable applications in rehabilitation requires an understanding of the current context, which is constantly changing over the rehabilitation process because of changes in the person's status and environment. This paper presents a dynamic recurrent neuro-fuzzy system that implements expert-and evidence-based reasoning. It is intended to provide context-awareness for wearable intelligent agents/assistants (WIAs). The model structure includes the following types of signals: inputs, states, outputs and outcomes. Inputs are facts or events which have effects on patients' physiological and rehabilitative states; different classes of inputs (e.g., facts, context, medication, therapy) have different nonlinear mappings to a fuzzy "effect." States are dimensionless linguistic fuzzy variables that change based on causal rules, as implemented by a fuzzy inference system (FIS). The FIS, with rules based on expertise and evidence, essentially defines the nonlinear state equations that are implemented by nuclei of dynamic neurons. Outputs, a function of weighing of states and effective inputs using conventional or fuzzy mapping, can perform actions, predict performance, or assist with decision-making. Outcomes are scalars to be extremized that are a function of outputs and states. The first example demonstrates setup and use for a large-scale stroke neurorehabilitation application (with 16 inputs, 12 states, 5 outputs and 3 outcomes), showing how this modelling tool can successfully capture causal dynamic change in context-relevant states (e.g., impairments, pain) as a function of input event patterns (e.g., medications). The second example demonstrates use of scientific evidence to develop rule-based dynamic models, here for predicting changes in muscle strength with short-term fatigue and long-term strength-training. A neuro-fuzzy modelling framework is developed for estimating rehabilitative change that can be applied in any field of rehabilitation
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Winters Jack M
2005-06-01
Full Text Available Abstract Background Intelligent management of wearable applications in rehabilitation requires an understanding of the current context, which is constantly changing over the rehabilitation process because of changes in the person's status and environment. This paper presents a dynamic recurrent neuro-fuzzy system that implements expert-and evidence-based reasoning. It is intended to provide context-awareness for wearable intelligent agents/assistants (WIAs. Methods The model structure includes the following types of signals: inputs, states, outputs and outcomes. Inputs are facts or events which have effects on patients' physiological and rehabilitative states; different classes of inputs (e.g., facts, context, medication, therapy have different nonlinear mappings to a fuzzy "effect." States are dimensionless linguistic fuzzy variables that change based on causal rules, as implemented by a fuzzy inference system (FIS. The FIS, with rules based on expertise and evidence, essentially defines the nonlinear state equations that are implemented by nuclei of dynamic neurons. Outputs, a function of weighing of states and effective inputs using conventional or fuzzy mapping, can perform actions, predict performance, or assist with decision-making. Outcomes are scalars to be extremized that are a function of outputs and states. Results The first example demonstrates setup and use for a large-scale stroke neurorehabilitation application (with 16 inputs, 12 states, 5 outputs and 3 outcomes, showing how this modelling tool can successfully capture causal dynamic change in context-relevant states (e.g., impairments, pain as a function of input event patterns (e.g., medications. The second example demonstrates use of scientific evidence to develop rule-based dynamic models, here for predicting changes in muscle strength with short-term fatigue and long-term strength-training. Conclusion A neuro-fuzzy modelling framework is developed for estimating
Evaluating Loans Using a Combination of Data Envelopment and Neuro-Fuzzy Systems
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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
Baraldi, Andrea; Binaghi, Elisabetta; Blonda, Palma N.; Brivio, Pietro A.; Rampini, Anna
1998-10-01
Mixed pixels, which do not follow a known statistical distribution that could be parameterized, are a major source of inconvenience in classification of remote sensing images. This paper reports on an experimental study designed for the in-depth investigation of how and why two neuro-fuzzy classification schemes, whose properties are complementary, estimate sub-pixel land cover composition from remotely sensed data. The first classifier is based on the fuzzy multilayer perceptron proposed by Pal and Mitra: the second classifier consists of a two-stage hybrid (TSH) learning scheme whose unsupervised first stage is based on the fully self- organizing simplified adaptive resonance theory clustering network proposed by Baraldi. Results of the two neuro-fuzzy classifiers are assessed by means of specific evaluation tools designed to extend conventional descriptive and analytical statistical estimators to the case of multi-membership in classes. When a synthetic data set consisting of pure and mixed pixels is processed by the two neuro-fuzzy classifiers, experimental result show that: i) the two neuro- fuzzy classifiers perform better than the traditional MLP; ii) classification accuracies of the two neuro-fuzzy classifiers are comparable; and iii) the TSH classifier requires to train less background knowledge than FMLP.
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G. Petchinathan
2014-06-01
Full Text Available This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT and an adaptive neuro-fuzzy inference system (ANFIS. The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC strategy are also presented.
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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)
Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Yatskovsky, Victor I.; Ogorodnik, K. V.; Lischenko, Sergey
2002-07-01
The perspective of neural networks equivalental models (EM) base on vector-matrix procedure with basic operations of continuous and neuro-fuzzy logic (equivalence, absolute difference) are shown. Capacity on base EMs exceeded the amount of neurons in 2.5 times. This is larger than others neural networks paradigms. Amount neurons of this neural networks on base EMs may be 10 - 20 thousands. The base operations in EMs are normalized equivalency operations. The family of new operations equivalency and non-equivalency of neuro-fuzzy logic's, which we have elaborated on the based of such generalized operations of fuzzy-logic's as fuzzy negation, t-norm and s-norm are shown. Generalized rules of construction of new functions (operations) equivalency which uses relations of t-norm and s-norm to fuzzy negation are proposed. Among these elements the following should be underlined: (1) the element which fulfills the operation of limited difference; (2) the element which algebraic product (intensifier with controlled coefficient of transmission or multiplier of analog signals); (3) the element which fulfills a sample summarizing (uniting) of signals (including the one during normalizing). Synthesized structures which realize on the basic of these elements the whole spectrum of required operations: t-norm, s-norm and new operations equivalency are shown. These realization on the basic of new multifunctional optoelectronical BISPIN- devices (MOEBD) represent the circuit with constant and pulse optical input signals. They are modeling the operation of limited difference. These circuits realize frequency- dynamic neuron models and neural networks. Experimental results of these MOEBD and equivalency circuits, which fulfill the limited difference operation are discussed. For effective realization of neural networks on the basic of EMs as it is shown in report, picture elements are required as main nodes to implement element operations equivalence ('non-equivalence') of neuro-fuzzy
Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model.
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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
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.
A Neuro-Fuzzy based System for Classification of Natural Textures
Jiji, G. Wiselin
2016-12-01
A statistical approach based on the coordinated clusters representation of images is used for classification and recognition of textured images. In this paper, two issues are being addressed; one is the extraction of texture features from the fuzzy texture spectrum in the chromatic and achromatic domains from each colour component histogram of natural texture images and the second issue is the concept of a fusion of multiple classifiers. The implementation of an advanced neuro-fuzzy learning scheme has been also adopted in this paper. The results of classification tests show the high performance of the proposed method that may have industrial application for texture classification, when compared with other works.
Condition monitoring with wind turbine SCADA data using Neuro-Fuzzy normal behavior models
DEFF Research Database (Denmark)
Schlechtingen, Meik; Santos, Ilmar
2012-01-01
in graphical and text format. Within the paper examples of real faults are provided, showing the capabilities of the method proposed. The method can be applied both to existing and new built turbines without the need of any additional hardware installation or manufacturers input.......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...
Dixon, B.
2005-07-01
Modeling groundwater vulnerability reliably and cost effectively for non-point source (NPS) pollution at a regional scale remains a major challenge. In recent years, Geographic Information Systems (GIS), neural networks and fuzzy logic techniques have been used in several hydrological studies. However, few of these research studies have undertaken an extensive sensitivity analysis. The overall objective of this research is to examine the sensitivity of neuro-fuzzy models used to predict groundwater vulnerability in a spatial context by integrating GIS and neuro-fuzzy techniques. The specific objectives are to assess the sensitivity of neuro-fuzzy models in a spatial domain using GIS by varying (i) shape of the fuzzy sets, (ii) number of fuzzy sets, and (iii) learning and validation parameters (including rule weights). The neuro-fuzzy models were developed using NEFCLASS-J software on a JAVA platform and were loosely integrated with a GIS. Four plausible parameters which are critical in transporting contaminants through the soil profile to the groundwater, included soil hydrologic group, depth of the soil profile, soil structure (pedality points) of the A horizon, and landuse. In order to validate the model predictions, coincidence reports were generated among model inputs, model predictions, and well/spring contamination data for NO 3-N. A total of 16 neuro-fuzzy models were developed for selected sub-basins of Illinois River Watershed, AR. The sensitivity analysis showed that neuro-fuzzy models were sensitive to the shape of the fuzzy sets, number of fuzzy sets, nature of the rule weights, and validation techniques used during the learning processes. Compared to bell-shaped and triangular-shaped membership functions, the neuro-fuzzy models with a trapezoidal membership function were the least sensitive to the various permutations and combinations of the learning and validation parameters. Over all, Models 11 and 8 showed relatively higher coincidence with well
Hardalaç, Firat
2008-04-01
Transcranial Doppler signals recorded from cerebral vessels of 110 patients were transferred to a personal computer by using a 16 bit sound card. Spectral analyses of Transcranial Doppler signals were performed for determining the Multi Layer Perceptron (MLP) neural network and neuro Ankara-fuzzy system inputs. In order to do a good interpretation and rapid diagnosis, FFT parameters of Transcranial Doppler signals classified using MLP neural network and neuro-fuzzy system. Our findings demonstrated that 92% correct classification rate was obtained from MLP neural network, and 86% correct classification rate was obtained from neuro-fuzzy system.
A neuro-fuzzy approach to the reliable recognition of electric earthquake precursors
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A. Konstantaras
2004-01-01
Full Text Available Electric Earthquake Precursor (EEP recognition is essentially a problem of weak signal detection. An EEP signal, according to the theory of propagating cracks, is usually a very weak electric potential anomaly appearing on the Earth's electric field prior to an earthquake, often unobservable within the electric background, which is significantly stronger and embedded in noise. Furthermore, EEP signals vary in terms of duration and size making reliable recognition even more difficult. An average model for EEP signals has been identified based on a time function describing the evolution of the number of propagating cracks. This paper describes the use of neuro-fuzzy networks (Neural Networks with intrinsic fuzzy logic abilities for the reliable recognition of EEP signals within the electric field. Pattern recognition is performed by the neural network to identify the average EEP model from within the electric field. Use of the neuro-fuzzy model enables classification of signals that are not exactly the same, but do approximate the average EEP model, as EEPs. On the other hand, signals that look like EEPs but do not approximate enough the average model are suppressed, preventing false classification. The effectiveness of the proposed network is demonstrated using electrotelluric data recorded in NW Greece.
A neuro-fuzzy approach to the reliable recognition of electric earthquake precursors
Konstantaras, A.; Varley, M. R.; Vallianatos, F.; Collins, G.; Holifield, P.
2004-10-01
Electric Earthquake Precursor (EEP) recognition is essentially a problem of weak signal detection. An EEP signal, according to the theory of propagating cracks, is usually a very weak electric potential anomaly appearing on the Earth's electric field prior to an earthquake, often unobservable within the electric background, which is significantly stronger and embedded in noise. Furthermore, EEP signals vary in terms of duration and size making reliable recognition even more difficult. An average model for EEP signals has been identified based on a time function describing the evolution of the number of propagating cracks. This paper describes the use of neuro-fuzzy networks (Neural Networks with intrinsic fuzzy logic abilities) for the reliable recognition of EEP signals within the electric field. Pattern recognition is performed by the neural network to identify the average EEP model from within the electric field. Use of the neuro-fuzzy model enables classification of signals that are not exactly the same, but do approximate the average EEP model, as EEPs. On the other hand, signals that look like EEPs but do not approximate enough the average model are suppressed, preventing false classification. The effectiveness of the proposed network is demonstrated using electrotelluric data recorded in NW Greece.
Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza
2014-10-01
The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications.
Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis.
Chang, Fi-John; Chung, Chang-Han; Chen, Pin-An; Liu, Chen-Wuing; Coynel, Alexandra; Vachaud, Georges
2014-10-01
We propose a systematical approach to assessing arsenic concentration in a river through: important factor extraction by a nonlinear factor analysis; arsenic concentration estimation by the neuro-fuzzy network; and impact assessment of important factors on arsenic concentration by the membership degrees of the constructed neuro-fuzzy network. The arsenic-contaminated Huang Gang Creek in northern Taiwan is used as a study case. Results indicate that rainfall, nitrite nitrogen and temperature are important factors and the proposed estimation model (ANFIS(GT)) is superior to the two comparative models, in which 50% and 52% improvements in RMSE are made over ANFIS(CC) and ANFIS(all), respectively. Results reveal that arsenic concentration reaches the highest in an environment of lower temperature, higher nitrite nitrogen concentration and larger one-month antecedent rainfall; while it reaches the lowest in an environment of higher temperature, lower nitrite nitrogen concentration and smaller one-month antecedent rainfall. It is noted that these three selected factors are easy-to-collect. We demonstrate that the proposed methodology is a useful and effective methodology, which can be adapted to other similar settings to reliably model water quality based on parameters of interest and/or study areas of interest for universal usage. The proposed methodology gives a quick and reliable way to estimate arsenic concentration, which makes good contribution to water environment management. Copyright © 2014 Elsevier B.V. All rights reserved.
Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network
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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.
Takagi-Sugeno Neuro-Fuzzy Modeling of a Multivariable Nonlinear Antenna System
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E. A. Al-Gallaf
2005-12-01
Full Text Available This article investigates the use of a clustered based neuro-fuzzy system to nonlinear dynamic system modeling. It is focused on the modeling via Takagi-Sugeno (T-S modeling procedure and the employment of fuzzy clustering to generate suitable initial membership functions. The T-S fuzzy modeling has been applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Compared to other well-known approximation techniques such as artificial neural networks, the employed neuro-fuzzy system has provided a more transparent representation of the nonlinear antenna system under study, mainly due to the possible linguistic interpretation in the form of rules. Created initial memberships are then employed to construct suitable T-S models. Furthermore, the T-S fuzzy models have been validated and checked through the use of some standard model validation techniques (like the correlation functions. This intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of the fuzzy if-then rules.
Digital Repository Service at National Institute of Oceanography (India)
Patil, S.G.; Mandal, S.; Hegde, A.V.; Alavandar, S.
University New York City, NY 10003-6603 and Systems Neurobiology Laboratory Salk Institute for Biological Studies La Jolla, CA 92037. A. Srinivasan and M.J. Nigam, Neuro-fuzzy based approach for inverse kinematics solution of industrial robot manipulators...
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 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)
Lledó, Luis D; Badesa, Francisco J; Almonacid, Miguel; Cano-Izquierdo, José M; Sabater-Navarro, José M; Fernández, Eduardo; Garcia-Aracil, Nicolás
2015-01-01
This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.
Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields
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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.
Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure
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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.
Analyzing the dynamics of emotional scene sequence using recurrent neuro-fuzzy network.
Zhang, Qing; Lee, Minho
2013-02-01
In this paper, we propose a new framework to analyze the temporal dynamics of the emotional stimuli. For this framework, both electroencephalography signal and visual information are of great importance. The fusion of visual information with brain signals allows us to capture the users' emotional state. Thus we adopt previously proposed fuzzy-GIST as emotional feature to summarize the emotional feedback. In order to model the dynamics of the emotional stimuli sequence, we develop a recurrent neuro-fuzzy network for modeling the dynamic events of emotional dimensions including valence and arousal. It can incorporate human expertise by IF-THEN fuzzy rule while recurrent connections allow the fuzzy rules of network to see its own previous output. The results show that such a framework can interact with human subjects and generate arbitrary emotional sequences after learning the dynamics of an emotional sequence with enough number of samples.
Almonacid, Miguel; Cano-Izquierdo, José M.; Sabater-Navarro, José M.; Fernández, Eduardo
2015-01-01
This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions. PMID:26001214
Multi Groups Cooperation based Symbiotic Evolution for TSK-type Neuro-Fuzzy Systems Design
Cheng, Yi-Chang; Hsu, Yung-Chi
2010-01-01
In this paper, a TSK-type neuro-fuzzy system with multi groups cooperation based symbiotic evolution method (TNFS-MGCSE) is proposed. The TNFS-MGCSE is developed from symbiotic evolution. The symbiotic evolution is different from traditional GAs (genetic algorithms) that each chromosome in symbiotic evolution represents a rule of fuzzy model. The MGCSE is different from the traditional symbiotic evolution; with a population in MGCSE is divided to several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperate with other groups to generate the better chromosomes by using the proposed cooperation based crossover strategy (CCS). In this paper, the proposed TNFS-MGCSE is used to evaluate by numerical examples (Mackey-Glass chaotic time series and sunspot number forecasting). The performance of the TNFS-MGCSE achieves excellently with other existing models in the simulations. PMID:21709856
A neuro-fuzzy system for extracting environment features based on ultrasonic sensors.
Marichal, Graciliano Nicolás; Hernández, Angela; Acosta, Leopoldo; González, Evelio José
2009-01-01
In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case.
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.
Gene regulatory network identification from the yeast cell cycle based on a neuro-fuzzy system.
Wang, B H; Lim, J W; Lim, J S
2016-08-30
Many studies exist for reconstructing gene regulatory networks (GRNs). In this paper, we propose a method based on an advanced neuro-fuzzy system, for gene regulatory network reconstruction from microarray time-series data. This approach uses a neural network with a weighted fuzzy function to model the relationships between genes. Fuzzy rules, which determine the regulators of genes, are very simplified through this method. Additionally, a regulator selection procedure is proposed, which extracts the exact dynamic relationship between genes, using the information obtained from the weighted fuzzy function. Time-series related features are extracted from the original data to employ the characteristics of temporal data that are useful for accurate GRN reconstruction. The microarray dataset of the yeast cell cycle was used for our study. We measured the mean squared prediction error for the efficiency of the proposed approach and evaluated the accuracy in terms of precision, sensitivity, and F-score. The proposed method outperformed the other existing approaches.
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach.
Ahmed, Sk Saddam; Dey, Nilanjan; Ashour, Amira S; Sifaki-Pistolla, Dimitra; Bălas-Timar, Dana; Balas, Valentina E; Tavares, João Manuel R S
2017-01-01
Crohn's disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifier and a neuro-fuzzy model are combined for diagnosing the CD. Factor analysis is used for data dimension reduction. The effect on the system performance has been investigated when using fuzzy partitioning and dimension reduction. Additionally, further comparison is done between the different levels of the fuzzy partition to reach the optimal performance accuracy level. The performance evaluation of the proposed system is estimated using the classification accuracy and other metrics. The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively.
Prediction of photonic crystal fiber characteristics by Neuro-Fuzzy system
Pourmahyabadi, M.; Mohammad Nejad, S.
2009-10-01
The most common methods applied in the analysis of photonic crystal fibers (PCFs) are finite difference time/frequency domain (FDTD/FDFD) method and finite element method (FEM). These methods are very general and reliable (well tested). They describe arbitrary structure but are numerically intensive and require detailed treatment of boundaries and complex definition of calculation mesh. So these conventional models that simulate the photonic response of PCFs are computationally expensive and time consuming. Therefore, a practical design process with trial and error cannot be done in a reasonable amount of time. In this article, an artificial intelligence method such as Neuro-Fuzzy system is used to establish a model that can predict the properties of PCFs. Simulation results show that this model is remarkably effective in predicting the properties of PCF such as dispersion, dispersion slope and loss over the C communication band.
A neuro-fuzzy price forecasting approach in deregulated electricity markets
Energy Technology Data Exchange (ETDEWEB)
Hong, Ying-Yi; Lee, Chuan-Fang [Department of Electrical Engineering, Chung Yuan Christian University, Chung Li 320 (Taiwan)
2005-02-01
Bidding competition is a main transaction approach in a deregulated market. Locational marginal prices (LMPs) resulting from bidding competition signal electricity values at a node or in an area. The LMP reveals important information for market participants to develop their bidding strategies. Moreover, LMP is also a vital indicator for the Security Coordinator to perform market redispatch for congestion management. This paper presents a method using fuzzy reasoning and recurrent neural networks (RNNs) for forecasting LMPs. The fuzzy rules are used to perform the linguistic reasoning about the contingencies. The reasoning results serve as a part of inputs to the RNNs for forecasting the LMPs. The historical LMPs in the PJM market are used to test the proposed method. It is found that the proposed neuro-fuzzy method is capable of forecasting LMP values efficiently.
Directory of Open Access Journals (Sweden)
Luis D Lledó
Full Text Available This paper presents the application of an Adaptive Resonance Theory (ART based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.
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.
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.
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.
Generic comparison of protein inference engines.
Claassen, Manfred; Reiter, Lukas; Hengartner, Michael O; Buhmann, Joachim M; Aebersold, Ruedi
2012-04-01
Protein identifications, instead of peptide-spectrum matches, constitute the biologically relevant result of shotgun proteomics studies. How to appropriately infer and report protein identifications has triggered a still ongoing debate. This debate has so far suffered from the lack of appropriate performance measures that allow us to objectively assess protein inference approaches. This study describes an intuitive, generic and yet formal performance measure and demonstrates how it enables experimentalists to select an optimal protein inference strategy for a given collection of fragment ion spectra. We applied the performance measure to systematically explore the benefit of excluding possibly unreliable protein identifications, such as single-hit wonders. Therefore, we defined a family of protein inference engines by extending a simple inference engine by thousands of pruning variants, each excluding a different specified set of possibly unreliable identifications. We benchmarked these protein inference engines on several data sets representing different proteomes and mass spectrometry platforms. Optimally performing inference engines retained all high confidence spectral evidence, without posterior exclusion of any type of protein identifications. Despite the diversity of studied data sets consistently supporting this rule, other data sets might behave differently. In order to ensure maximal reliable proteome coverage for data sets arising in other studies we advocate abstaining from rigid protein inference rules, such as exclusion of single-hit wonders, and instead consider several protein inference approaches and assess these with respect to the presented performance measure in the specific application context.
Generic Comparison of Protein Inference Engines*
Claassen, Manfred; Reiter, Lukas; Hengartner, Michael O.; Buhmann, Joachim M.; Aebersold, Ruedi
2012-01-01
Protein identifications, instead of peptide-spectrum matches, constitute the biologically relevant result of shotgun proteomics studies. How to appropriately infer and report protein identifications has triggered a still ongoing debate. This debate has so far suffered from the lack of appropriate performance measures that allow us to objectively assess protein inference approaches. This study describes an intuitive, generic and yet formal performance measure and demonstrates how it enables experimentalists to select an optimal protein inference strategy for a given collection of fragment ion spectra. We applied the performance measure to systematically explore the benefit of excluding possibly unreliable protein identifications, such as single-hit wonders. Therefore, we defined a family of protein inference engines by extending a simple inference engine by thousands of pruning variants, each excluding a different specified set of possibly unreliable identifications. We benchmarked these protein inference engines on several data sets representing different proteomes and mass spectrometry platforms. Optimally performing inference engines retained all high confidence spectral evidence, without posterior exclusion of any type of protein identifications. Despite the diversity of studied data sets consistently supporting this rule, other data sets might behave differently. In order to ensure maximal reliable proteome coverage for data sets arising in other studies we advocate abstaining from rigid protein inference rules, such as exclusion of single-hit wonders, and instead consider several protein inference approaches and assess these with respect to the presented performance measure in the specific application context. PMID:22057310
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)
Kayacan, Erkan; Kayacan, Erdal; Ramon, Herman; Saeys, Wouter
2013-02-01
As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations.
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
A Comparative Analysis of Fuzzy Inference Engines in Context of ...
African Journals Online (AJOL)
PROF. O. E. OSUAGWU
profitability quantification in plastic recycling. [14] designs a neuro-fuzzy linguistic approach in optimizing the flow rate of a plastic extruder process. [15] presents fuzzy rule-base frame work for the management of tropical diseases. [16] proposes a fuzzy-neural network model for effective control of profitability in a paper.
REPLACEMENT SPARE PART INVENTORY MONITORING USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM
National Research Council Canada - National Science Library
Hartono Hartono
2016-01-01
... 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...
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...
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
Directory of Open Access Journals (Sweden)
Mithaq Nama Raheema
2017-07-01
Full Text Available The design of ANFIS network based inverse control technique is proposed in this paperfor this system. Simulation is implemented in MATLAB after the ANFIS is trained and it is shown that results are applicable in process industry and acceptable for reference control applications. The effectiveness of the proposed ANFIS in inverse controller it has been tested by entering random selected points which represent the values of input voltage from the system under control as a reference input to inverse modelling, after that entering the results of inverse modelling to the modelling of magnet levitation system to form the desired output. The result is acceptable with small errors about 0.0011
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
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%.
Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun
2012-01-01
With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…
Tao, Yang; Li, Yong; Zhou, Ruiyun; Chu, Dinh-Toi; Su, Lijuan; Han, Yongbin; Zhou, Jianzhong
2016-10-01
In the study, osmotically dehydrated cherry tomatoes were partially dried to water activity between 0.746 and 0.868, vacuum-packed and stored at 4-30 °C for 60 days. Adaptive neuro-fuzzy inference system (ANFIS) was utilized to predict the physicochemical and microbiological parameters of these partially dried cherry tomatoes during storage. Satisfactory accuracies were obtained when ANFIS was used to predict the lycopene and total phenolic contents, color and microbial contamination. The coefficients of determination for all the ANFIS models were higher than 0.86 and showed better performance for prediction compared with models developed by response surface methodology. Through ANFIS modeling, the effects of storage conditions on the properties of partially dried cherry tomatoes were visualized. Generally, contents of lycopene and total phenolics decreased with the increase in water activity, temperature and storage time, while aerobic plate count and number of yeasts and molds increased at high water activities and temperatures. Overall, ANFIS approach can be used as an effective tool to study the quality decrease and microbial pollution of partially dried cherry tomatoes during storage, as well as identify the suitable preservation conditions.
Taghadomi-Saberi, Saeedeh; Omid, Mahmoud; Emam-Djomeh, Zahra; Ahmadi, Hojjat
2014-01-15
This paper presents a versatile way for estimating antioxidant activity and anthocyanin content at different ripening stages of sweet cherry by combining image processing and two artificial intelligence (AI) techniques. In comparison with common time-consuming laboratory methods for determining these important attributes, this new way is economical and much faster. The accuracy of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models was studied to estimate the outputs. Sensitivity analysis and principal component analysis were used with ANN and ANFIS respectively to specify the most effective attributes on outputs. Among the designed ANNs, two hidden layer networks with 11-14-9-1 and 11-6-20-1 architectures had the highest correlation coefficients and lowest error values for modeling antioxidant activity (R = 0.93) and anthocyanin content (R = 0.98) respectively. ANFIS models with triangular and two-term Gaussian membership functions gave the best results for antioxidant activity (R = 0.87) and anthocyanin content (R = 0.90) respectively. Comparison of the models showed that ANN outperformed ANFIS for this case. By considering the advantages of the applied system and the accuracy obtained in somewhat similar studies, it can be concluded that both techniques presented here have good potential to be used as estimators of proposed attributes. © 2013 Society of Chemical Industry.
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.
Directory of Open Access Journals (Sweden)
Saleh Shahinfar
2012-01-01
Full Text Available Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.
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.
Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A.
2012-01-01
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. PMID:22991575
Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease.
Badnjevic, Almir; Cifrek, Mario; Koruga, Dragan; Osmankovic, Dinko
2015-01-01
This paper presents a system for classification of asthma and chronic obstructive pulmonary disease (COPD) based on fuzzy rules and the trained neural network. Fuzzy rules and neural network parameters are defined according to Global Initiative for Asthma (GINA) and Global Initiative for chronic Obstructive Lung Disease (GOLD) guidelines. For neural network training more than one thousand medical reports obtained from database of the company CareFusion were used. Afterwards the system was validated on 455 patients by physicians from the Clinical Centre University of Sarajevo. Out of 170 patients with asthma, 99.41% of patients were correctly classified. In addition, 99.19% of the 248 COPD patients were correctly classified. The system was 100% successful on 37 patients with normal lung function. Sensitivity of 99.28% and specificity of 100% in asthma and COPD classification were obtained. Our neuro-fuzzy system for classification of asthma and COPD uses a combination of spirometry and Impulse Oscillometry System (IOS) test results, which in the very beginning enables more accurate classification. Additionally, using bronchodilatation and bronhoprovocation tests we get a complete patient's dynamic assessment, as opposed to the solution that provides a static assessment of the patient.
Macroscopic Rock Texture Image Classification Using a Hierarchical Neuro-Fuzzy Class Method
Directory of Open Access Journals (Sweden)
Laercio B. Gonçalves
2010-01-01
Full Text Available We used a Hierarchical Neuro-Fuzzy Class Method based on binary space partitioning (NFHB-Class Method for macroscopic rock texture classification. The relevance of this study is in helping Geologists in the diagnosis and planning of oil reservoir exploration. The proposed method is capable of generating its own decision structure, with automatic extraction of fuzzy rules. These rules are linguistically interpretable, thus explaining the obtained data structure. The presented image classification for macroscopic rocks is based on texture descriptors, such as spatial variation coefficient, Hurst coefficient, entropy, and cooccurrence matrix. Four rock classes have been evaluated by the NFHB-Class Method: gneiss (two subclasses, basalt (four subclasses, diabase (five subclasses, and rhyolite (five subclasses. These four rock classes are of great interest in the evaluation of oil boreholes, which is considered a complex task by geologists. We present a computer method to solve this problem. In order to evaluate system performance, we used 50 RGB images for each rock classes and subclasses, thus producing a total of 800 images. For all rock classes, the NFHB-Class Method achieved a percentage of correct hits over 73%. The proposed method converged for all tests presented in the case study.
Phase Angle Control of Three Level Inverter Based D-STATCOM Using Neuro-Fuzzy Controller
Directory of Open Access Journals (Sweden)
COTELI, R.
2012-02-01
Full Text Available Distribution Static Compensator (D-STATCOM is a shunt compensation device used to improve electric power quality in distribution systems. It is well-known that D-STATCOM is a nonlinear, semi-defined and time-varying system. Therefore, control of D-STATCOM by the conventional control techniques is very difficult task. In this paper, the control of D-STATCOM is carried out by the neuro-fuzzy controller (NFC which has non-linear and robust structure. For this aim, an experimental setup based on three-level H-bridge inverter is constructed. Phase angle control method is used for control of D-STATCOM's output reactive power. Control algorithm for this experimental setup is prepared in MATLAB/Simulink and downloaded to DS1103 controller card. A Mamdani type NFC is designed for control of D-STATCOM's reactive current. Output of NFC is integrated to increase tracking performance of controller in steady state. The performance of D-STATCOM is experimentally evaluated by changing reference reactive current as on-line. The experimental results show that the proposed controller gives very satisfactory performance under different loading conditions.
Dynamical recurrent neuro-fuzzy identification schemes employing switching parameter hopping.
Theodoridis, Dimitrios; Boutalis, Yiannis; Christodoulou, Manolis
2012-04-01
In this paper we analyze the identification problem which consists of choosing an appropriate identification model and adjusting its parameters according to some adaptive law, such that the response of the model to an input signal (or a class of input signals), approximates the response of the real system for the same input. For identification models we use fuzzy-recurrent high order neural networks. High order networks are expansions of the first-order Hopfield and Cohen-Grossberg models that allow higher order interactions between neurons. The underlying fuzzy model is of Mamdani type assuming a standard defuzzification procedure such as the weighted average. Learning laws are proposed which ensure that the identification error converges to zero exponentially fast or to a residual set when a modeling error is applied. There are two core ideas in the proposed method: (1) Several high order neural networks are specialized to work around fuzzy centers, separating in this way the system into neuro-fuzzy subsystems, and (2) the use of a novel method called switching parameter hopping against the commonly used projection in order to restrict the weights and avoid drifting to infinity.
Applying a neuro-fuzzy approach for transient identification in a nuclear power plant
Energy Technology Data Exchange (ETDEWEB)
Costa, Rafael G.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.; Carvalho, Paulo V.R., E-mail: rgcosta@ien.gov.b, E-mail: mol@ien.gov.b, E-mail: cmnap@ien.gov.b, E-mail: paulov@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)
2009-07-01
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)
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.
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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.
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.
A Multitarget Tracking Video System Based on Fuzzy and Neuro-Fuzzy Techniques
Directory of Open Access Journals (Sweden)
Javier I. Portillo
2005-08-01
Full Text Available Automatic surveillance of airport surface is one of the core components of advanced surface movement, guidance, and control systems (A-SMGCS. This function is in charge of the automatic detection, identification, and tracking of all interesting targets (aircraft and relevant ground vehicles in the airport movement area. This paper presents a novel approach for object tracking based on sequences of video images. A fuzzy system has been developed to ponder update decisions both for the trajectories and shapes estimated for targets from the image regions extracted in the images. The advantages of this approach are robustness, flexibility in the design to adapt to different situations, and efficiency for operation in real time, avoiding combinatorial enumeration. Results obtained in representative ground operations show the system capabilities to solve complex scenarios and improve tracking accuracy. Finally, an automatic procedure, based on neuro-fuzzy techniques, has been applied in order to obtain a set of rules from representative examples. Validation of learned system shows the capability to learn the suitable tracker decisions.
Hybrid neuro-fuzzy system for power generation control with environmental constraints
Energy Technology Data Exchange (ETDEWEB)
Chaturvedi, Krishna Teerth; Pandit, Manjaree; Srivastava, Laxmi [Department of Electrical Engineering, Madhav Institute of Technology and Science (M.I.T.S.), Race Course Road, Gola Ka Mandir, Gwalior, Madhya Pradesh 474 005 (India)
2008-11-15
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. (author)
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.
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.
Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A
2012-01-01
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.
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. PMID:26881269
Carvalho, Lucimar M F de; Nassar, Silvia Modesto; Azevedo, Fernando Mendes de; Carvalho, Hugo José Teixeira de; Monteiro, Lucas Lese; Rech, Ciciliana M Zílio
2008-06-01
To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication) architecture and an artificial neural network with backpropagation learning algorithm (ANNB). 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%. 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.
Liu, Feng; Quek, Chai; Ng, Geok See
2007-06-01
There are two important issues in neuro-fuzzy modeling: (1) interpretability--the ability to describe the behavior of the system in an interpretable way--and (2) accuracy--the ability to approximate the outcome of the system accurately. As these two objectives usually exert contradictory requirements on the neuro-fuzzy model, certain compromise has to be undertaken. This letter proposes a novel rule reduction algorithm, namely, Hebb rule reduction, and an iterative tuning process to balance interpretability and accuracy. The Hebb rule reduction algorithm uses Hebbian ordering, which represents the degree of coverage of the samples by the rule, as an importance measure of each rule to merge the membership functions and hence reduces the number of the rules. Similar membership functions (MFs) are merged by a specified similarity measure in an order of Hebbian importance, and the resultant equivalent rules are deleted from the rule base. The rule with a higher Hebbian importance will be retained among a set of rules. The MFs are tuned through the least mean square (LMS) algorithm to reduce the modeling error. The tuning of the MFs and the reduction of the rules proceed iteratively to achieve a balance between interpretability and accuracy. Three published data sets by Nakanishi (Nakanishi, Turksen, & Sugeno, 1993), the Pat synthetic data set (Pal, Mitra, & Mitra, 2003), and the traffic flow density prediction data set are used as benchmarks to demonstrate the effectiveness of the proposed method. Good interpretability, as well as high modeling accuracy, are derivable simultaneously and are suitably benchmarked against other well-established neuro-fuzzy models.
Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.
Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios
2013-10-01
In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not
Das, Arpita; Bhattacharya, Mahua
2011-01-01
In the present work, authors have developed a treatment planning system implementing genetic based neuro-fuzzy approaches for accurate analysis of shape and margin of tumor masses appearing in breast using digital mammogram. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology, genetic algorithm (GA) has been used for searching of effective input feature vectors combined with adaptive neuro-fuzzy model for final classification of different boundaries of tumor masses. The study involves 200 digitized mammograms from MIAS and other databases and has shown 86% correct classification rate.
Constantinescu, Alina
2017-12-01
The aim of our study is to improve the crop planning procedures using neuro-fuzzy concepts. In this paper we design a neuro-fuzzy procedure that offers the suitable maize hybrid, from a set of preferred hybrids, which must be organically farmed in the current year. Our method is a statistical one, on the one hand it processes data provided by the previous years and on the other hand it takes in account the vague character of the environmental factors. Also we present here some experimental results obtained by us on a certain set of real data, results which prove the efficiency of our approach.
Directory of Open Access Journals (Sweden)
A.K. Parida
2016-09-01
Full Text Available In this paper Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system is presented for the prediction and analysis of financial and electrical energy market data. The normally used TSK-type feedforward fuzzy neural network is unable to take the full advantage of the use of the linear fuzzy rule base in accurate input–output mapping and hence the consequent part of the rule base is made nonlinear using polynomial or arithmetic basis functions. Further the Chebyshev polynomial functions provide an expanded nonlinear transformation to the input space thereby increasing its dimension for capturing the nonlinearities and chaotic variations in financial or energy market data streams. Also the locally recurrent neuro-fuzzy information system (LRNFIS includes feedback loops both at the firing strength layer and the output layer to allow signal flow both in forward and backward directions, thereby making the LRNFIS mimic a dynamic system that provides fast convergence and accuracy in predicting time series fluctuations. Instead of using forward and backward least mean square (FBLMS learning algorithm, an improved Firefly-Harmony search (IFFHS learning algorithm is used to estimate the parameters of the consequent part and feedback loop parameters for better stability and convergence. Several real world financial and energy market time series databases are used for performance validation of the proposed LRNFIS model.
Risk analysis of lung cancer and effects of stress level on cancer risk through neuro-fuzzy model.
Yılmaz, Atınç; Arı, Seçkin; Kocabıçak, Ümit
2016-12-01
A significant number of people pass away due to limited medical resources for the battle with cancer. Fatal cases can be reduced by using the computational techniques in the medical and health system. If the cancer is diagnosed early, the chance of successful treatment increases. In this study, the risk of getting lung cancer will be obtained and patients will be provided with directions to exterminate the risk. After calculating the risk value for lung cancer, status of the patient's susceptibility and resistance to stress is used in determining the effects of stress to disease. In order to resolve the problem, the neuro-fuzzy logic model has been presented. When encouraging results are obtained from the study; the system will form a pre-diagnosis for the people who possibly can have risk of getting cancer due to working conditions or living standards. Therefore, this study will enable these people to take precautions to prevent the risk of cancer. In this study a new t-norm operator has been utilized in the problem. Finally, the performance of the proposed method has been compared to other methods. Beside this, the contribution of neuro-fuzzy logic model in the field of health and topics of artificial intelligence will also be examined in this study. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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.
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
Development of an intelligent neuro-fuzzy maneuver identification system for autonomous aircraft
Krishnamurthy, Karthik
2000-10-01
This dissertation reports an investigation of the design of intelligent systems for the high-level control of autonomous aircraft. In a departure from recent work in this field, an attempt has been made to synthesize a high-level control architecture that emulates a human pilot's reasoning capabilities. The system architecture uses pilot-type classifications of aircraft modes (the various maneuvers that pilots are trained to execute) within all decision-making and reasoning processes. A flight control system structured in terms of these modes offers scope for efficient combination of concepts from artificial intelligence, control theory and aviation practice. A critical component of this intelligent flight controller is an automated mode inference system. This innovative system extracts high-level knowledge of the current maneuver (or segment of the overall mission) from sensed measurements of dynamic state variables. Using a blend of soft computing approaches, this inference engine consistently identifies the correct maneuver being flown, even in the presence of moderate sensor noise and data ambiguities. In the process of creating this inference engine, a novel scheme to generate training data sets for neural networks has been developed. This data generation scheme permits complete coverage of the aircraft's capability envelope; this coverage is achieved without recourse to the voluminous flight data (actual or simulated) normally required to train neural networks. The data generation scheme thus significantly reduces developmental effort. Apart from this innovation, pilot-like techniques to cope with the phenomenon of chatter (where identification rapidly switches back-and-forth between modes) have been developed and implemented within the inference system. This dissertation also discusses the development of logic to interpret and implement commands from remote operators, using high-level knowledge of the current mission segment. This knowledge is used to
Energy Technology Data Exchange (ETDEWEB)
Chaabene, Maher; Ben Ammar, Mohsen [The High Institute of Technological Studies (ISET), Sfax (Tunisia); Unite de commande de machines et energies renouvelables CMER, ENIS (Tunisia)
2008-07-15
This paper introduces a dynamic forecasting of irradiance and ambient temperature. The medium term forecasting (MTF) gives a daily meteorological behaviour. It consists of a neuro-fuzzy estimator based on meteorological parameters' behaviours during the days before, and on time distribution models. As for the short term forecasting (STF), it estimates, for a 5 min time step ahead, the meteorological parameters evolution. It is ensured by the Auto-Regressive Moving Average (ARMA) model of the MTF associated to a Kalman filter. STF uses instantaneous measured data, delivered by a data acquisition system, so as to accomplish the forecast. Herein we describe our method and we present forecasting results. Validation is based on measurements taken at the Energy and Thermal Research Centre (CRTEn) in the north of Tunisia. Since our work delivers accurate meteorological parameters forecasting, the obtained results can be easily adapted to forecast any solar conversion system output. (author)
A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system.
Lim, Jongwoo; Wang, Bohyun; Lim, Joon S
2016-04-29
Finding the minimum number of appropriate biomarkers for specific targets such as a lung cancer has been a challenging issue in bioinformatics. We propose a hierarchical two-phase framework for selecting appropriate biomarkers that extracts candidate biomarkers from the cancer microarray datasets and then selects the minimum number of appropriate biomarkers from the extracted candidate biomarkers datasets with a specific neuro-fuzzy algorithm, which is called a neural network with weighted fuzzy membership function (NEWFM). In this context, as the first phase, the proposed framework is to extract candidate biomarkers by using a Bhattacharyya distance method that measures the similarity of two discrete probability distributions. Finally, the proposed framework is able to reduce the cost of finding biomarkers by not receiving medical supplements and improve the accuracy of the biomarkers in specific cancer target datasets.
Introducing an Evolving Local Neuro-Fuzzy Model--Application to modeling of car-following behavior.
Kazemi, Reza; Abdollahzade, Majid
2015-11-01
This paper proposes an Evolving Local Linear Neuro-Fuzzy Model for modeling and identification of nonlinear time-variant systems which change their nature and character over time. The proposed approach evolves through time to follow the structural changes in the time-variant dynamic systems. The evolution process is managed by a distance-based extended hierarchical binary tree algorithm, which decides whether the proposed evolving model should be adapted to the system variations or evolution is necessary. To represent an interesting but challenging example of the systems with changing dynamics, the proposed evolving model is applied to model car-following process in a traffic flow, as an online identification problem. Results of simulations demonstrate effectiveness of the proposed approach in modeling of the time-variant systems. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Computational statistics using the Bayesian Inference Engine
Weinberg, Martin D.
2013-09-01
This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimized software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organize and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasizes hybrid tempered Markov chain Monte Carlo schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE implements a full persistence or serialization system that stores the full byte-level image of the running inference and previously characterized posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU General Public License.
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.
A Comparative Analysis of Fuzzy Inference Engines in Context of ...
African Journals Online (AJOL)
PROF. O. E. OSUAGWU
robotics, pattern recognition, etc. This paper presents a comparative analysis of three fuzzy inference engines, max-product, max-min and root sum in fuzzy controllers using profitability control data. The presented results shows that RSS inference engine gives largest output membership function, while the product inference ...
Aproximación neuro-fuzzy para identificación de señales viales mediante tecnología infrarroja
Directory of Open Access Journals (Sweden)
G.N. Marichal
2007-04-01
Full Text Available Resumen: En este artículo se presenta un sistema basado en tecnología infrarroja para la clasificación de marcas viales empleando un sistema Neuro-Fuzzy como herramienta de clasificación. El sistema se ha testeado a partir de los datos suministrados cuando se ha instalado un prototipo en un robot móvil. Los resultados obtenidos son explicados en este artículo, haciendo hincapié en el diseño de nuevas reglas y la mejoría lograda mediante los métodos propuestos. Palabras clave: Control Inteligente, Robótica, Navegación de robots, Sistemas Neuro-Fuzzy
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 ...
Directory of Open Access Journals (Sweden)
F. Sdao
2013-02-01
Full Text Available 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
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
Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi
2007-04-01
SummaryModeling of rainfall-runoff dynamics is one of the most studied topics in hydrology due to its essential application to water resources management. Recently, artificial intelligence has gained much popularity for calibrating the nonlinear relationships inherent in the rainfall-runoff process. In this study, the advantages of artificial neural networks and neuro-fuzzy system in continuous modeling of the daily and hourly behaviour of runoff were examined. Three different adaptive techniques were constructed and examined namely, Levenberg-Marquardt feed forward neural network, Bayesian regularization feed forward neural network, and neuro-fuzzy. In addition, the effects of data transformation on model performance were also investigated. This was done by examining the performance of the three network architectures and training algorithms using both raw and transformed data. Through inspection of the results it was found that although the model built on transformed data outperforms the model built on raw data, no significant differences were found between the forecast accuracies of the three examined models. A detailed comparison of the overall performance indicated that the neuro-fuzzy model performed better than both the Levenberg-Marquardt-FFNN and the Bayesian regularization-FFNN. In order to enable users to process the data easily, a graphic user interface (GUI) was developed. This program allows users to process the rainfall-runoff data, to train/test the model using various input options and to visualize results.
Dehkordi, Azimeh N V; Kamali-Asl, Alireza; Wen, Ning; Mikkelsen, Tom; Chetty, Indrin J; Bagher-Ebadian, Hassan
2017-09-01
This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimated by the model selection (MS) technique in dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data analysis, and patient age. DCE-MRI investigations of 33 treatment-naïve patients with GBM were studied. Using the modified Tofts model and MS technique, the following physiologically nested models were constructed: Model 1, no vascular leakage (normal tissue); Model 2, leakage without efflux; Model 3, leakage with bidirectional exchange (influx and efflux). For each patient, the PK parameters of the three models were estimated as follows: blood plasma volume (vp ) for Model 1; vp and volume transfer constant (K(trans) ) for Model 2; vp , K(trans) and rate constant (kep ) for Model 3. Using Cox regression analysis, the best combination of the estimated PK parameters, together with patient age, was identified for the design and training of ANFIS. A K-fold cross-validation (K = 33) technique was employed for training, testing and optimization of ANFIS. Given the survival time distribution, three classes of survival were determined and a confusion matrix for the correct classification fraction (CCF) of the trained ANFIS was estimated as an accuracy index of ANFIS's performance. Patient age, kep and ve (K(trans) /kep ) of Model 3, and K(trans) of Model 2, were found to be the most effective parameters for training ANFIS. The CCF of the trained ANFIS was 84.8%. High diagonal elements of the confusion matrix (81.8%, 90.1% and 81.8% for Class 1, Class 2 and Class 3, respectively), with low off-diagonal elements, strongly confirmed the robustness and high performance of the trained ANFIS for predicting the three survival classes. This study confirms that DCE-MRI PK analysis, combined with the MS technique and ANFIS
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
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V. M. Pakhomovа
2016-12-01
Full Text Available Purpose. Continuous increase in network traffic in the information-telecommunication system (ITS of Prydniprovsk Railways leads to the need to determine the real-time network congestion and to control the data flows. One of the possible solutions is a method of forecasting the volume of network traffic (inbound and outbound using neural network technology that will prevent from server overload and improve the quality of services. Methodology. Analysis of current network traffic in ITS of Prydniprovsk Railways and preparation of sets: learning, test and validation ones was conducted as well as creation of neuro-fuzzy network (hybrid system in Matlab program and organization of the following phases on the appropriate sets: learning, testing, forecast adequacy analysis. Findings. For the fragment (Dnipropetrovsk – Kyiv in ITS of Prydniprovsk Railways we made a forecast (day ahead for volume of network traffic based on the hybrid system created in Matlab program; MAPE values are as follows: 6.9% for volume of inbound traffic; 7.7% for volume of outbound traffic. It was found that the average learning error of the hybrid system decreases in case of increase in: the number of inputs (from 2 to 4; the number of terms (from 2 to 5 of the input variable; learning sample power (from 20 to 100. A significant impact on the average learning error of the hybrid system is caused by the number of terms of its input variable. It was determined that the lowest value of the average learning error is provided by 4-input hybrid system, it ensures more accurate learning of the neuro-fuzzy network by the hybrid method. Originality. The work resulted in the dependences for the average hybrid system error of the network traffic volume forecasting for the fragment (Dnipropetrovsk-Kyiv in ITS Prydniprovsk Railways on: the number of its inputs, the number of input variable terms, the learning sample power for different learning methods. Practical value. Forecasting of
An Ultrasonic Multi-Beam Concentration Meter with a Neuro-Fuzzy Algorithm for Water Treatment Plants
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Ho-Hyun Lee
2015-10-01
Full Text Available 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.
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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.
On Principles of Software Engineering -- Role of the Inductive Inference
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Ladislav Samuelis
2012-01-01
Full Text Available This paper highlights the role of the inductive inference principle in software engineering. It takes the challenge to settle differences and to confront the ideas behind the usual software engineering concepts. We focus on the inductive inference mechanism’s role behind the automatic program construction activities and software evolution. We believe that the revision of rather old ideas in the new context of software engineering could enhance our endeavour and that is why deserves more attention.
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 ... 4. ANFIS: We designed ANFIS based on two linguistic variables distance d and direction h1 and h2 of the topics. During offline training, membership function parameters are chosen that best allow the fuzzy inference system to match with classifier output. During online testing phase, linguistic parameters d,.
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
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...
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...
Gurrala Madhusudhan Rao
2014-01-01
Abstract: The main theme of the paper which deals with the enhancing steady-state and dynamics performance of the power grids by Flexible AC Transmission System (FACTS) based on computational intelligence. The proposed technique will be applied to solve real problems in a power grid. The FACTS device, which will be used in the paper, is the most promising one, which known as the Distributed Power Flow Controller (DPFC). The paper achieves the optimization of the type, the location and the siz...
Kurtulus, Bedri; Razack, Moumtaz
2010-02-01
SummaryThis paper compares two methods for modeling karst aquifers, which are heterogeneous, highly non-linear, and hierarchical systems. There is a clear need to model these systems given the crucial role they play in water supply in many countries. In recent years, the main components of soft computing (fuzzy logic (FL), and Artificial Neural Networks, (ANNs)) have come to prevail in the modeling of complex non-linear systems in different scientific and technologic disciplines. In this study, Artificial Neural Networks and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods were used for the prediction of daily discharge of karstic aquifers and their capability was compared. The approach was applied to 7 years of daily data of La Rochefoucauld karst system in south-western France. In order to predict the karst daily discharges, single-input (rainfall, piezometric level) vs. multiple-input (rainfall and piezometric level) series were used. In addition to these inputs, all models used measured or simulated discharges from the previous days with a specified delay. The models were designed in a Matlab™ environment. An automatic procedure was used to select the best calibrated models. Daily discharge predictions were then performed using the calibrated models. Comparing predicted and observed hydrographs indicates that both models (ANN and ANFIS) provide close predictions of the karst daily discharges. The summary statistics of both series (observed and predicted daily discharges) are comparable. The performance of both models is improved when the number of inputs is increased from one to two. The root mean square error between the observed and predicted series reaches a minimum for two-input models. However, the ANFIS model demonstrates a better performance than the ANN model to predict peak flow. The ANFIS approach demonstrates a better generalization capability and slightly higher performance than the ANN, especially for peak discharges.
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Radovanović Milan M.
2015-01-01
Full Text Available In this research we search for a functional dependence between the occurrence of forest fires in the USA and the factors which characterize the solar activity. For this purpose we used several methods (R/S analysis, Hurst index to establish potential links between the influx of some parameters from the sun and the occurrence of forest fires with lag of several days. We found evidence for a connection and developed a prognostic scenario based on the Adaptive neuro-fuzzy interference system (ANFIS technique. This scenario allows the prediction between 79-93% of forest fires. [Projekat Ministarstva nauke Republike Srbije, br. III47007
Automated Flight Safety Inference Engine (AFSIE) System Project
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...
Abnormal red blood cells detection using adaptive neuro-fuzzy system.
Babazadeh Khameneh, Nahid; Arabalibeik, Hossein; Salehian, Piruz; Setayeshi, Saeed
2012-01-01
Features like size, shape, and volume of red blood cells are important factors in diagnosing related blood disorders such as iron deficiency and anemia. This paper proposes a method to detect abnormality in red blood cells using cell microscopic images. Adaptive local thresholding and bounding box methods are used to extract inner and outer diameters of red cells. An adaptive network-based fuzzy inference system (ANFIS) is used to classify blood samples to normal and abnormal. Accuracy of the proposed method and area under ROC curve are 96.6% and 0.9950 respectively.
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Gligor Alina
2017-01-01
Full Text Available The paper analyzes the possibility to use a neuro-fuzzy type mathematical model, with the final goal of establishing the welding parameters for new types and dimensions of pipes and fittings. Anticipating the developing dynamic of polyethylene-made elements, especially pipes and fittings, starting from the current situation when already a wide range of pipes and fittings with different wall thicknesses and nominal working pressures is produced and commercialized, and taking into account also new development, it was considered necessary to find out the welding parameters for any new pipe type and dimension. The usage of existing welding equipment for new pipe dimensions is impossible without a preliminary set of welding parameters: pressure, temperature, time. Based on experimentally validated data for discreet values of the characteristic welding parameters, there was generated, using mathematical laws and functions, a new model that can estimate the necessary values of the welding parameters for any value within their variation range. As a result, the mathematical model created using neuro-fuzzy techniques allows the obtaining of the correct value for certain parameters (e.g. required welding pressure for any values of the input variables pipe diameter and pipe thickness.
Liu, Cheng-Li
2009-05-01
Only a few studies in the literature have focused on the effects of age on virtual environment (VE) sickness susceptibility and even less research was carried out focusing on the elderly. In general, the elderly usually browse VEs on a thin film transistor liquid crystal display (TFT-LCD) at home or somewhere, not a head-mounted display (HMD). While the TFT-LCD is used to present VEs, this set-up does not physically enclose the user. Therefore, this study investigated the factors that contribute to cybersickness among the elderly when immersed into a VE on TFT-LCD, including exposure durations, navigation rotating speeds and angle of inclination. Participants were elderly, with an average age of 69.5 years. The results of the first experiment showed that the rate of simulator sickness questionnaire (SSQ) scores increases significantly with navigational rotating speed and duration of exposure. However, the experimental data also showed that the rate of SSQ scores does not increase with the increase in angle of inclination. In applying these findings, the neuro-fuzzy technology was used to develop a neuro-fuzzy cybersickness-warning system integrating fuzzy logic reasoning and neural network learning. The contributing factors were navigational rotating speed and duration of exposure. The results of the second experiment showed that the proposed system can efficiently determine the level of cybersickness based on the associated subjective sickness estimates and combat cybersickness due to long exposure to a VE.
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.
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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.
Energy Technology Data Exchange (ETDEWEB)
Sarrafan, Atabak; Zareh, Seiyed Hamid; Khayyat, Amir Ali Akbar; Zabihollah, Abolghassem [Sharif University of Technology, Teheran (Iran, Islamic Republic of)
2012-04-15
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.
A Hybrid Stochastic-Neuro-Fuzzy Model-Based System for In-Flight Gas Turbine Engine Diagnostics
2001-04-05
for safety (performance) degradation are introduced. The cumulative reliability sensitivity index ( CRSI ) is defined by the "global" non-dimensional...index 42 below 3.7 (fault probability of 0.0001), for a CRSI of 0.5, or equivalently for a CRRI of 2.0 and for a ERSI of 0.2, or equivalently for a...Reliability Index Fig. 11. Fault Probabilities 44 The cumulative and evolutionary reliability sensitivity indices, CRSI and ERSI, which describe the
Inference Engine in an Intelligent Ship Course-Keeping System
Directory of Open Access Journals (Sweden)
Piotr Borkowski
2017-01-01
Full Text Available The article presents an original design of an expert system, whose function is to automatically stabilize ship’s course. The focus is put on the inference engine, a mechanism that consists of two functional components. One is responsible for the construction of state space regions, implemented on the basis of properly processed signals recorded by sensors from the input and output of an object. The other component is responsible for generating a control decision based on the knowledge obtained in the first module. The computing experiments described herein prove the effective and correct operation of the proposed system.
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Giuseppe Casalino
2013-01-01
Full Text Available Weld imperfections are tolerable defects as stated from the international standard. Nevertheless they can produce a set of drawbacks like difficulty to assembly, reworking, limited fatigue life, and surface imperfections. In this paper Ti6Al4V titanium butt welds were produced by CO2 laser welding. The following tolerable defects were analysed: weld undercut, excess weld metal, excessive penetration, incomplete filled groove, root concavity, and lack of penetration. A neuro-fuzzy model for the prediction and classification of the defects in the fused zone was built up using the experimental data. Weld imperfections were connected to the welding parameters by feed forward neural networks. Then the imperfections were clustered using the C-means fuzzy clustering algorithm. The clusters were named after the ISO standard classification of the levels of imperfection for electron and laser beam welding of aluminium alloys and steels. Finally, a single-value metric was proposed for the assessment of the overall bead geometry quality. It combined an index for each defect and functioned according to the criterion “the-smallest-the-best.”
Theodoridis, Dimitrios; Boutalis, Yiannis; Christodoulou, Manolis
2010-04-01
The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new neuro-fuzzy dynamical systems description, which uses the fuzzy partitioning of an underlying fuzzy systems outputs and high order neural networks (HONN's) associated with the centers of these partitions. Every high order neural network approximates a group of fuzzy rules associated with each center. The indirect regulation is achieved by first identifying the system around the current operation point, and then using its parameters to device the control law. Weight updating laws for the involved HONN's are provided, which guarantee that, under the presence of both parameter and dynamic uncertainties, both the identification error and the system states reach zero, while keeping all signals in the closed loop bounded. The control signal is constructed to be valid for both square and non square systems by using a pseudoinverse, in Moore-Penrose sense. The existence of the control signal is always assured by employing a novel method of parameter hopping instead of the conventional projection method. The applicability is tested on well known benchmarks.
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.
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Noel García-Díaz
2016-11-01
Full Text Available In the software development field, software practitioners expend between 30% and 40% more effort than is predicted. Accordingly, researchers have proposed new models for estimating the development effort such that the estimations of these models are close to actual ones. In this study, an application based on a new neuro-fuzzy system (NFS is analyzed. The NFS accuracy was compared to that of a statistical multiple linear regression (MLR model. The criterion for evaluating the accuracy of estimation models has mainly been the Magnitude of Relative Error (MRE, however, it was recently found that MRE is asymmetric, and the use of Absolute Residuals (AR has been proposed, therefore, in this study, the accuracy results of the NFS and MLR were based on AR. After a statistical paired t-test was performed, results showed that accuracy of the New-NFS is statistically better than that of the MLR at the 99% confidence level. It can be concluded that a new-NFS could be used for predicting the effort of software development projects when they have been individually developed on a disciplined process.
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
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 Behnamfard
2017-06-01
Full Text Available The proximate analysis is the most common form of coal evaluation and it reveals the quality of a coal sample. It examines four factors including the moisture, ash, volatile matter (VM, and fixed carbon (FC within the coal sample. Every factor is determined through a distinct experimental procedure under ASTM specified conditions. These determinations are time consuming and require a significant amount of laboratory equipment. The calorific value is one of the most important properties of a solid fuel and its experimental determination requires special instrumentation and highly trained analyst to operate it. This paper develops mathematical and ANFIS models for estimation of two factors of proximate analysis based on the other two factors. Furthermore, the estimation of calorific value of coal samples based on proximate analysis factors is performed using multivariable regression, the Minitab 16 software package, and the ANFIS, Matlab software package. The results indicate that ANFIS is a more powerful tool for estimation of proximate analysis factors and calorific value than multivariable regression method. The following equation estimates the calorific value of coal samples with high precision: Calorific value (btu/lb= 12204 - 170 Moisture + 46.8 FC - 127 Ash
Ghaedi, M; Ghaedi, A M; Abdi, F; Roosta, M; Vafaei, A; Asghari, A
2013-10-01
In the present study, activated carbon (AC) simply derived from Pistacia khinjuk and characterized using different techniques such as SEM and BET analysis. This new adsorbent was used for methylene blue (MB) adsorption. Fitting the experimental equilibrium data to various isotherm models shows the suitability and applicability of the Langmuir model. The adsorption mechanism and rate of processes was investigated by analyzing time dependency data to conventional kinetic models and it was found that adsorption follow the pseudo-second-order kinetic model. Principle component analysis (PCA) has been used for preprocessing of input data and genetic algorithm optimization have been used for prediction of adsorption of methylene blue using activated carbon derived from P. khinjuk. In our laboratory various activated carbon as sole adsorbent or loaded with various nanoparticles was used for removal of many pollutants (Ghaedi et al., 2012). These results indicate that the small amount of proposed adsorbent (1.0g) is applicable for successful removal of MB (RE>98%) in short time (45min) with high adsorption capacity (48-185mgg(-1)). Copyright © 2013 Elsevier Inc. All rights reserved.
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Ricardo Pinto Ferreira
2011-01-01
Full Text Available The increase in consumption by Brazilian families, a consequence of the economic stability experienced in the country in recent years, has resulted in an increase in the volume of items that need to be picked up and delivered daily in the city of São Paulo. This situation has led to profound changes in the market for the pickup and delivery of orders, making the distribution highly complex and directly affecting the efficiency of this service. Diverse techniques and software, some based on artificial intelligence, are used to predict the behavior of vehicular urban traffic in the São Paulo metropolitan region. In this paper, artificial neural networks were combined with fuzzy logic to form a neuro-fuzzy network in order to predict the behavior of traffic. The results indicate that the application of the neuro-fuzzy network for predicting the behavior of urban vehicular traffic in the city of São Paulo yields positive results.
DEFF Research Database (Denmark)
Møller, Jesper
.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......(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...... is on spatial point processes....
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.
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.
Automated interpretation of LIBS spectra using a fuzzy logic inference engine.
Hatch, Jeremy J; McJunkin, Timothy R; Hanson, Cynthia; Scott, Jill R
2012-03-01
Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. Fuzzy logic inference rules were developed using methodology that includes data mining methods and operator expertise to differentiate between various copper-containing and stainless steel alloys as well as unknowns. Results using the fuzzy logic inference engine indicate a high degree of confidence in spectral assignment. © 2012 Optical Society of America
DNA Implementation of Fuzzy Inference Engine: Towards DNA Decision-Making Systems.
George, Aby K; Singh, Harpreet
2017-10-09
Decision-making systems are an integral part of any autonomous device. With the recent developments in bionanorobots, smart drugs, and engineered viruses, there is an immediate need of decision-making systems which are biocompatible in nature. DNA is considered a perfect candidate for designing the computing systems in such decision-making systems because of their bio-compatibility and programmability. Complex biological systems can be easily modeled/controlled using fuzzy logic operations with the help of linguistic rules. In this paper, we propose an enzyme-free DNA strand displacement based architecture of fuzzy inference engine using the fuzzy operators such as fuzzy intersection and union. The basic building blocks of this architecture are minimum, maximum and fan-out gates. All these gates are analog in nature, which means that the input/output values of the gates are represented by the concentration of the input/output DNA strands. To demonstrate the performance of the proposed architecture, a detailed design, analysis, and kinetic simulation of each gate were carried out. Finally, the minimum and maximum gates are cascaded according to the pre-defined rules to design the fuzzy inference engine. All these DNA circuits are implemented and simulated in Visual DSD software.
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.
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
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)
SHAIK Mohammad Naushad
2017-12-08
Dec 8, 2017 ... Parkinson's disease (Kumudini et al. 2014; Kirbas et al. 2016), etc. Hyperhomocysteinaemia could occur due to genetic polymorphisms/mutations or cofactor deficiencies of the folate metabolic pathway. The dietary source of folate is in the form of folyl polyglutamate and is converted to monoglutamates with.
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 ...
ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers
César, Manuel Braz; Barros, Rui Carneiro
2016-11-01
In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS. Fuzzy logic based controllers are capable to deal with non-linear or uncertain systems, which make them particularly well suited for civil engineering applications. The main objective is to develop a semi-active control system with a MR damper to reduce the response of a three degrees-of-freedom (DOFs) building structure. The control system is designed using ANFIS to optimize the fuzzy inference rule of a simple fuzzy logic controller. The results show that the proposed semi-active neuro-fuzzy based controller is effective in reducing the response of structural system.
Zhang, Daili
Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications
ADAPTIF NEUROFUZZY INFERENCE SYSTEM UNTUK PENGUKURAN pH
Directory of Open Access Journals (Sweden)
Totok R. Biyanto
2006-01-01
Full Text Available Due to increasing of measurement performance necessity and pH controlling on laboratory scale and industry, it needs to make measurement system that can give the best performance with the high accuracy and robust with the disturbance and noise. Sensor of pH measurement is combination electrode with voltage output. This is influenced by hydrogen ion and liquid temperature. It will lead error if there is temperature changing in the measured process. In order to solve this problem, it needs pH measurement device that can measure pH without disturbed by temperature changes. ANFIS (Adaptive Neuro Fuzzy Inference System can be used for identifying the voltage due from hydrogen ion and temperature changes. So, the pH measurement would be robust to temperature changes. Based on this result study, precision and accuracy device are 1.91% and 0.45% in the pH range 2-10.6 and temperature range 10-80oC. This gives conclusion that ANFIS can decrease pH reading error because of temperature changes. Abstract in Bahasa Indonesia : Seiring dengan meningkatnya kebutuhan performansi pengukuran dan pengendalian pH baik dalam skala laboratorium maupun industri maka perlu dibuat sistem pengukuran yang mampu memberikan performansi yang baik dengan akurasi yang memadai dan tahan terhadap ganguan dan noise. Sensor yang digunakan pada pengukuran pH adalah elektroda kombinasi dengan output tegangan. Tegangan tersebut selain dipengruhi oleh kehadiran ion hidrogen juga sangat dipengaruhi oleh temperatur larutan, sehingga akan terjadi kesalahan yang berarti bila ada perubahan temperatur pada proses yang diukur. Untuk mengatasi hal tersebut maka perlu dibuat alat ukur pH yang mampu mengukur pH tanpa terpengaruh oleh perubahan temperatur. Salah satu caranya adalah dengan memanfaatkan ANFIS (Adaptif NeuroFuzzy Inference System untuk mengindentifikasi tegangan yang dihasilkan oleh kehadiran ion hidrogen dan tegangan yang disebabkan oleh perubahan temperartur, sehingga hasil
MAZIE: a mass and charge inference engine to enhance database searching of tandem mass spectra.
Victor, Ken G; Murgai, Meera; Lyons, Charles E; Templeton, Thaddeus A B; Moshnikov, Sergey A; Templeton, Dennis J
2010-01-01
Peptide sequence identification using tandem mass spectroscopy remains a major challenge for complex proteomic studies. Peptide matching algorithms require the accurate determination of both the mass and charge of the precursor ion and accommodate uncertainties in these properties by using a wide precursor mass tolerance and by testing, for each spectrum, several possible candidate charges. Using a data acquisition strategy that includes obtaining narrow mass-range MS(1) "zoom" scans, we describe here a post-acquisition algorithm dubbed mass and charge (Z) inference engine (MAZIE), which accurately determines the charge and monoisotopic mass of precursor ions on a low-resolution Thermo LTQ-XL mass spectrometer. This is achieved by examining the isotopic distribution obtained in the preceding MS(1) zoom spectrum and comparing to theoretical distributions for candidate charge states from +1 to +4. MAZIE then writes modified data files with the corrected monoisotopic mass and charge. We have validated MAZIE results by comparing the sequence search results obtained with the MAZIE-generated data files to results using the unmodified data files. Using two different search algorithms and a false discovery rate filter, we found that MAZIE-interpreted data resulted in 80% (using SEQUEST) and 30% (using OMSSA) more high-confidence sequence identifications. Analyses of these results indicate that the accurate determination of the precursor ion mass greatly facilitates the ability to differentiate between true and false positive matches, while the determination of the precursor ion charge reduces the overall search time but does not significantly reduce the ambiguity of interpreting the search results. MAZIE is distributed as an open-source PERL script. 2010 American Society for Mass Spectrometry. Published by Elsevier Inc. All rights reserved.
Ghanei, Amir; Jafari, Faezeh; Mehrinejad Khotbehsara, Mojdeh; Mohseni, Ehsan; Cui, Hongzhi
2017-01-01
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 water
Energy Technology Data Exchange (ETDEWEB)
Castro, Adriana R. Garcez; Miranda, Vladimiro [Instituto de Engenharia de Sistemas e Computadores do Porto, INESC Porto (Portugal)
2005-12-01
An artificial neural network concept has been developed for transformer fault diagnosis using dissolved gas-in-oil analysis (DGA). A new methodology for mapping the neural network into a rule-based inference system is described. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a Fuzzy Inference System. Some studies are reported, illustrating the good results obtained. (author)
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
2009-01-01
Appendix I) 0047-03-C2 Neuro - Fuzzy Probabilistic Systems For Fault Diagnosis of Turbine Engines From 1 Sep 07 – 27 Nov 08, technical support was...Data-Driven Methods:- Mo et al. [3], propose a fuzzy inference logic system for gas turbine engine fault isolation. They state their reasoning...for selecting a fuzzy inference logic system as; 1) the fuzzy logic system is a knowledge based system that has ability to handle uncertainty, 2) the
JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services
Eshratifar, Amir Erfan; Abrishami, Mohammad Saeed; Pedram, Massoud
2018-01-01
Deep neural networks are among the most influential architectures of deep learning algorithms, being deployed in many mobile intelligent applications. End-side services, such as intelligent personal assistants (IPAs), autonomous cars, and smart home services often employ either simple local models or complex remote models on the cloud. Mobile-only and cloud-only computations are currently the status quo approaches. In this paper, we propose an efficient, adaptive, and practical engine, JointD...
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.
Google Earth Engine derived areal extents to infer elevation variation of lakes and reservoirs
Nguy-Robertson, Anthony; May, Jack; Dartevelle, Sebastien; Griffin, Sean; Miller, Justin; Tetrault, Robert; Birkett, Charon; Lucero, Eileen; Russo, Tess; Zentner, Matthew
2017-04-01
Monitoring water supplies is important for identifying potential national security issues before they begin. As a means to estimate lake and reservoir storage for sites without reliable water stage data, this study defines correlations between water body levels from hypsometry curves based on in situ gauge station and altimeter data (i.e. TOPEX/Poseidon, Jason series) and sensor areal extents observed in historic multispectral (i.e. MODIS and Landsat TM/ETM+/OLI) imagery. Water levels measured using in situ observations and altimeters, when in situ data were unavailable, were used to estimate the relationship between water elevation and surface area for 18 sites globally. Altimeters were generally more accurate (RMSE: 0.40 - 0.49 m) for estimating in situ lake elevations from Iraq and Afghanistan than the modeled elevation data using multispectral sensor areal extents: Landsat (RMSE: 0.25 - 1.5 m) and MODIS (RMSE 0.53 - 3.0 m). Correlations between altimeter data and Landsat imagery processed with Google Earth Engine confirmed similar relationships exists for a broader range of lakes without reported in situ data across the globe (RMSE: 0.24 - 1.6 m). Thus, while altimetry is still preferred to an areal extent model, lake surface area derived with Google Earth Engine can be used as a reasonable proxy for lake storage, expanding the number of observable lakes beyond the current constellation of altimeters and in situ gauges.
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
Neuro-fuzzy model for evaluating the performance of processes ...
Indian Academy of Sciences (India)
CHIDOZIE CHUKWUEMEKA NWOBI-OKOYE
2017-11-16
Nov 16, 2017 ... and Ai (or Biю2) is a linguistic variable associated with this node. Therefore O1,i is the membership grade of a fuzzy set. (A1, A2, B1, B2). Layer 2. Every node in this layer is a fixed node labelled as Prod. The output is the product of all the incoming signals: O2,i ¼ wi ¼ lAixП чlBiyП ч for i ¼ 1, 2. П13ч.
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 ...
A neuro-fuzzy architecture for real-time applications
Ramamoorthy, P. A.; Huang, Song
1992-01-01
Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.
A NEURO FUZZY MODEL FOR THE INVESTIGATION OF ...
African Journals Online (AJOL)
The ultimate goal is the ability to access the current and future life of oil pipe, given a set of circumstances, and also appropriate adoptable methodology in view of a preventive maintenance measure for the pipes in a given operating environment. Results reveal that with more than 40% clay content quickens corrosion of ...
Characterizing root distribution with adaptive neuro-fuzzy analysis
Root-soil relationships are pivotal to understanding crop growth and function in a changing environment. Plant root systems are difficult to measure and remain understudied relative to above ground responses. High variation among field samples often leads to non-significance when standard statistics...
Adaptive neuro-fuzzy system for malware detection | Sodiya ...
African Journals Online (AJOL)
ANFSMD utilizes both the Application Programming Interface (API) calls and operation codes to study the behaviour of Portable Executable (PE) files. The PE files were disassembled into low-level codes and the identified features were grouped for efficient detection. Five features, selected using weighted average, were ...
Intelligent neuro fuzzy expert system for autism recognition | Obi ...
African Journals Online (AJOL)
Autism is a brain disorder that is associated with a wide range of developmental problems, especially in communication, social interaction and unusual repetitive behavior. However, it is ... The proposed system which is self-learning and adaptive is able to handle the uncertainties often associated with the diagnosis 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)
National Research Council Canada - National Science Library
Includes papers in the following fields: Aerospace Engineering, Agricultural Engineering, Chemical Engineering, Civil Engineering, Electrical Engineering, Environmental Engineering, Industrial Engineering, Materials Engineering, Mechanical...
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.
Langmore, Ian; Davis, Anthony B.; Bal, Guillaume; Marzouk, Youssef M.
2012-01-01
We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single low resolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as "multi-pixel" techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used.
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
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.
Flatness-based embedded adaptive fuzzy control of turbocharged diesel engines
Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan
2014-10-01
In this paper nonlinear embedded control for turbocharged Diesel engines is developed with the use of Differential flatness theory and adaptive fuzzy control. It is shown that the dynamic model of the turbocharged Diesel engine is differentially flat and admits dynamic feedback linearization. It is also shown that the dynamic model can be written in the linear Brunovsky canonical form for which a state feedback controller can be easily designed. To compensate for modeling errors and external disturbances an adaptive fuzzy control scheme is implemanted making use of the transformed dynamical system of the diesel engine that is obtained through the application of differential flatness theory. Since only the system's output is measurable the complete state vector has to be reconstructed with the use of a state observer. It is shown that a suitable learning law can be defined for neuro-fuzzy approximators, which are part of the controller, so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed observer-based adaptive fuzzy control scheme results in H∞ tracking performance.
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...
Fuzzy neural approach for colon cancer prediction | Obi | Scientia ...
African Journals Online (AJOL)
fuzzy inference procedure. The proposed system which is self-learning and adaptive is able to handle the uncertainties often associated with the diagnosis and analysis of colon cancer. Keywords: Neural Network, Fuzzy logic, Neuro Fuzzy System, ...
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
Swift: Compiled Inference for Probabilistic Programming Languages
Wu, Yi; Li, Lei; Russell, Stuart; Bodik, Rastislav
2016-01-01
A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-...
How Do Statistical Detection Methods Compare to Entropy Measures
2012-08-28
Qingzhong Liu, Andrew H. Sung, "Detect Information-Hiding Type and Length in JPEG Images by Using Neuro - fuzzy Inference Systems ," cisp, vol. 5, pp...Computer Science, 2009 [13] Qingzhong Liu, Andrew H. Sung, "Detect Information-Hiding Type and Length in JPEG Images by Using Neuro - fuzzy ...possible to break every steganaographic system that has ever been published. As a result of this, new techniques are developed to improve upon the
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 framework...... is composed of a set of language primitives and of an inference engine based on a message-passing system that integrates cutting-edge computational tools, including proximal algorithms and high performance Hamiltonian Markov Chain Monte Carlo techniques. A set of domain-specific highly optimized GPU......-accelerated primitives specializes iLang to the spatial data-structures that arise in imaging applications. We illustrate the framework through a challenging application: spatio-temporal tomographic reconstruction with compressive sensing....
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. .
Bayesian inference for agreement measures.
Vidal, Ignacio; de Castro, Mário
2016-08-25
The agreement of different measurement methods is an important issue in several disciplines like, for example, Medicine, Metrology, and Engineering. In this article, some agreement measures, common in the literature, were analyzed from a Bayesian point of view. Posterior inferences for such agreement measures were obtained based on well-known Bayesian inference procedures for the bivariate normal distribution. As a consequence, a general, simple, and effective method is presented, which does not require Markov Chain Monte Carlo methods and can be applied considering a great variety of prior distributions. Illustratively, the method was exemplified using five objective priors for the bivariate normal distribution. A tool for assessing the adequacy of the model is discussed. Results from a simulation study and an application to a real dataset are also reported.
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.
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 a...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....... 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 (so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last...
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.
Genetic Neuro-Fuzzy System for the Intelligent Recognition of Stroke ...
African Journals Online (AJOL)
Genetic algorithm is used for optimizing fuzzy set or rules, neural network provides the self-learning paradigm while fuzzy logic handles vagueness or imprecision of fuzzy set. The evaluation results show an effective way of determining and assessing the three different levels of stroke. This provides a decision support for the ...
A comparative study of ANN and Neuro-fuzzy for the prediction of ...
Indian Academy of Sciences (India)
Fuzzy set theory, Fuzzy logic and Neural Networks techniques seem very well suited for typical geotechnical problems. In conjunction with statistics and conventional mathematical methods, hybrid methods can be developed that may prove to be a step forward in modeling geotechnical problems. Here, we have developed ...
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.
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.
Progressive low-bitrate digital color/monochrome image coding by neuro-fuzzy clustering
Mitra, Sunanda; Meadows, Steven
1997-10-01
Color image coding at low bit rates is an area of research that is just being addressed in recent literature since the problems of storage and transmission of color images are becoming more prominent in many applications. Current trends in image coding exploit the advantage of subband/wavelet decompositions in reducing the complexity in optimal scalar/vector quantizer (SQ/VQ) design. Compression ratios (CRs) of the order of 10:1 to 20:1 with high visual quality have been achieved by using vector quantization of subband decomposed color images in perceptually weighted color spaces. We report the performance of a recently developed adaptive vector quantizer, namely, AFLC-VQ for effective reduction in bit rates while maintaining high visual quality of reconstructed color as well as monochrome images. For 24 bit color images, excellent visual quality is maintained upto a bit rate reduction to approximately 0.48 bpp (for each color plane or monochrome 0.16 bpp, CR 50:1) by using the RGB color space. Further tuning of the AFLC-VQ, and addition of an entropy coder module after the VQ stage results in extremely low bit rates (CR 80:1) for good quality, reconstructed images. Our recent study also reveals that for similar visual quality, RGB color space requires less bits/pixel than either the YIQ, or HIS color space for storing the same information when entropy coding is applied. AFLC-VQ outperforms other standard VQ and adaptive SQ techniques in retaining visual fidelity at similar bit rate reduction.
A neuro-fuzzy approach to the reliable recognition of electric earthquake precursors
Konstantaras, A.; Varley, M. R.; Vallianatos, F.; Collins, G.; Holifield, P.
2004-01-01
Electric Earthquake Precursor (EEP) recognition is essentially a problem of weak signal detection. An EEP signal, according to the theory of propagating cracks, is usually a very weak electric potential anomaly appearing on the Earth's electric field prior to an earthquake, often unobservable within the electric background, which is significantly stronger and embedded in noise. Furthermore, EEP signals vary in terms of duration and size making reliable recognition even more difficult. An aver...
A neuro-fuzzy approach to the reliable recognition of electric earthquake precursors
A. Konstantaras; M. R. Varley; F. Vallianatos; G. Collins; P. Holifield
2004-01-01
International audience; Electric Earthquake Precursor (EEP) recognition is essentially a problem of weak signal detection. An EEP signal, according to the theory of propagating cracks, is usually a very weak electric potential anomaly appearing on the Earth's electric field prior to an earthquake, often unobservable within the electric background, which is significantly stronger and embedded in noise. Furthermore, EEP signals vary in terms of duration and size making reliable recognition even...
Clustering of noisy image data using an adaptive neuro-fuzzy system
Pemmaraju, Surya; Mitra, Sunanda
1992-01-01
Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.
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.
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 ...
Use of an adaptive neuro-fuzzy system to characterize root distribution patterns
Root-soil relationships are pivotal to understanding crop growth and function in a changing environmental. Plant root systems are difficult to measure and remain understudied relative to above ground responses. High variation among field samples often leads to non-significance when standard statist...
D.R.Kalbande; Priyank Singhal; Nilesh Deotale; Sumiran Shah; G.T.Thampi
2011-01-01
Selecting an optimum advanced technology system for an organization is one of the most crucial issues in any industry. Any technology system which makes business process more efficient and business management more simplified is one of the important Information System (IS) to the organization. The comprehensive framework is a three-phase approach which introduces two main ideas, one is the adopting of the McCall software quality model which is extracted from technology management essentials, a...
Predictability in space launch vehicle anomaly detection using intelligent neuro-fuzzy systems
Gulati, Sandeep; Toomarian, Nikzad; Barhen, Jacob; Maccalla, Ayanna; Tawel, Raoul; Thakoor, Anil; Daud, Taher
1994-01-01
Included in this viewgraph presentation on intelligent neuroprocessors for launch vehicle health management systems (HMS) are the following: where the flight failures have been in launch vehicles; cumulative delay time; breakdown of operations hours; failure of Mars Probe; vehicle health management (VHM) cost optimizing curve; target HMS-STS auxiliary power unit location; APU monitoring and diagnosis; and integration of neural networks and fuzzy logic.
A comparative study of ANN and neuro-fuzzy for the prediction of ...
Indian Academy of Sciences (India)
Reply 2. It was used because the system was relatively smooth and trained to cover all likely events. Also, we carried out a parametric analysis with different. MF types and found that Gaussian MF is working well. Reply 3. Our intention was to show readers only about the deviation of predicted value from the observed value.
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…
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
Nonlinear Adaptive NeuroFuzzy Wavelet Based Damping Control Paradigm for SSSC
Directory of Open Access Journals (Sweden)
BADAR, R.
2012-08-01
Full Text Available Static Synchronous Series Compensator (SSSC is a series compensating Flexible AC Transmission System (FACTS controller with primary objective of power flow control on a line by injecting a voltage in series with transmission line. However, it can efficiently be used for improving the system stability by using a supplementary damping control system. In this work, Adaptive Neurofuzzy Wavelet Control (ANFWC paradigm for SSSC supplementary damping control system has been proposed and successfully applied to a Single Machine Infinite Bus (SMIB power system. Gradient descent based back propagation algorithm, being simple with sufficient efficiency, has been used to update the controller parameters. The robustness of the proposed control strategy has been validated using nonlinear time domain simulations for different faults and various operating conditions of power system. Finally, the results have been compared with Conventional Adaptive Takagi-Sugino Controller (CATC on the basis of different performance indices.
A comparative study of ANN and Neuro-fuzzy for the prediction of ...
Indian Academy of Sciences (India)
Fuzzy set theory, Fuzzy logic and. Neural Networks techniques seem very well suited for typical geotechnical problems. In conjunction with statistics and conventional mathematical methods, hybrid methods can be developed that may prove to be a step forward in modeling geotechnical problems. Here, we have developed ...
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.
Mulder, J.M.
2013-01-01
It is often claimed that principles of individuation imply essential properties of the things individuated. For example, sets are individuated by their members, hence sets have their members essentially. But how does this inference work? First I discuss the form of such inferences, and conclude that
Stan Development Team
2018-01-01
Stan facilitates statistical inference at the frontiers of applied statistics and provides both a modeling language for specifying complex statistical models and a library of statistical algorithms for computing inferences with those models. These components are exposed through interfaces in environments such as R, Python, and the command line.
The Importance of Statistical Modeling in Data Analysis and Inference
Rollins, Derrick, Sr.
2017-01-01
Statistical inference simply means to draw a conclusion based on information that comes from data. Error bars are the most commonly used tool for data analysis and inference in chemical engineering data studies. This work demonstrates, using common types of data collection studies, the importance of specifying the statistical model for sound…
Collective Inference with Learned and Engineered Knowledge
2009-07-17
aggregated features, and fme -grained levels of an ontology reduce the statistical bias associated with aggregating potentially dissimilar objects. Either of...probability trees, interactive interpreter for our Python-based scripting language, and the ability to import and export XML- formatted data Proximity 4.0...physics derived from the abstract and citation files provided for the 2003 KDD Cup competition. The original datasets are from arXiv, an electronic
Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling
Energy Technology Data Exchange (ETDEWEB)
Afshar, Mohammad [Islamic Azad University, Kharg (Iran, Islamic Republic of); Gholami, Amin [Petroleum University of Technology, Abadan (Iran, Islamic Republic of); Asoodeh, Mojtaba [Islamic Azad University, Birjand (Iran, Islamic Republic of)
2014-03-15
Bubble point pressure is a critical pressure-volume-temperature (PVT) property of reservoir fluid, which plays an important role in almost all tasks involved in reservoir and production engineering. We developed two sophisticated models to estimate bubble point pressure from gas specific gravity, oil gravity, solution gas oil ratio, and reservoir temperature. Neural network and adaptive neuro-fuzzy inference system are powerful tools for extracting the underlying dependency of a set of input/output data. However, the mentioned tools are in danger of sticking in local minima. The present study went further by optimizing fuzzy logic and neural network models using the genetic algorithm in charge of eliminating the risk of being exposed to local minima. This strategy is capable of significantly improving the accuracy of both neural network and fuzzy logic models. The proposed methodology was successfully applied to a dataset of 153 PVT data points. Results showed that the genetic algorithm can serve the neural network and neuro-fuzzy models from local minima trapping, which might occur through back-propagation algorithm.
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
Statistical inferences in phylogeography
DEFF Research Database (Denmark)
Nielsen, Rasmus; Beaumont, Mark A
2009-01-01
In conventional phylogeographic studies, historical demographic processes are elucidated from the geographical distribution of individuals represented on an inferred gene tree. However, the interpretation of gene trees in this context can be difficult as the same demographic/geographical process ...
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...... of updated programs. We have formalized a concept of transformation parts that serve as an indication of when a change specification is evident in a set of changes. Based on the transformation parts concept, we state a subsumption relation on change specifications. The subsumption relation allows decision...... specifications inferred by spdiff in Linux are shown. We find that the inferred specifications concisely capture the actual collateral evolution performed in the examples....
Type Inference with Inequalities
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff
1991-01-01
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...
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.
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...
1991-07-01
9) The yacht was often used for social and political events by several presidents until Carter disposed of it. and people don’t seem to have trouble...over an undergrad. 3. World knowledge. For example, one may use facts about the social rules of dating to infer that the most likely interpretation of (1...of equivalence clases , while the semantics of every should be based on pairs. A more illuminating solution would be to assume an ambiguity
Consistency and Plausible Inference,
1982-10-01
than a syllogism . Again, experts can operate in environments containing inconsistent or contradictory "facts," but such environments are useless in...than- categorical affirmation of B even when A is known with certainty. A useful way of viewing this formalism is as an inference net [Hayes-Roth...34, the precise interpretation of these numbers is open. It is intended that a proposition or relation with validity l be equivalent to a categorical
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.
Causal inference in multisensory perception
National Research Council Canada - National Science Library
Körding, Konrad P; Beierholm, Ulrik; Ma, Wei Ji; Quartz, Steven; Tenenbaum, Joshua B; Shams, Ladan
2007-01-01
.... Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location...
Continuous Integrated Invariant Inference Project
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...
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.
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.
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)
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
Directory of Open Access Journals (Sweden)
I. A. Ahmad
2012-07-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE In the current work, some well-known inference procedures including testing and estimation are adjusted to accommodate noisy data that lead to nonidentically distributed sample. The main two cases addressed are the Poisson and the normal distributions. Both one and two sample cases are addressed. Other cases including the exponential and the Pareto distributions are briefly mentioned. In the Poisson case, the situation when the sample size is random is mentioned.
Nanotechnology and statistical inference
Vesely, Sara; Vesely, Leonardo; Vesely, Alessandro
2017-08-01
We discuss some problems that arise when applying statistical inference to data with the aim of disclosing new func-tionalities. A predictive model analyzes the data taken from experiments on a specific material to assess the likelihood that another product, with similar structure and properties, will exhibit the same functionality. It doesn't have much predictive power if vari-ability occurs as a consequence of a specific, non-linear behavior. We exemplify our discussion on some experiments with biased dice.
Appraisal of ANN and ANFIS for Predicting Vertical Total Electron ...
African Journals Online (AJOL)
The propagation of the GPS signals are interfered by free electrons which are the massive particles in the ionosphere region and results in delays in the ... Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms have been developed for the prediction of VTEC in the ionosphere.
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...
An Intelligent Clustering Based Methodology for Confusable ...
African Journals Online (AJOL)
An Intelligent Clustering Based Methodology for Confusable Diseases Diagnosis and Monitoring. ... In this paper, an intelligent system driven by fuzzy clustering algorithm and Adaptive Neuro-Fuzzy Inference System for the investigation, diagnosis and management of similar and confusing symptoms of confusable diseases ...
African Journals Online (AJOL)
Konditi, DBO. Vol 15, No 1 (2013) - Articles Application of adaptive neuro-fuzzy inference system technique in design of rectangular microstrip patch antennas. Abstract · Vol 13, No 2 (2011) - Articles Closed‐Loop transmit diversity (transmit beamforming) for mitigation of interference and multipath fading in wireless ...
Extraction of fetal electrocardiogram (ECG) by extended state ...
Indian Academy of Sciences (India)
Fetal electrocardiogram (ECG) gives information about the health status of fetus and so, an early diagnosis of any cardiac defect before delivery increases the effectiveness of appropriate treatment. In this paper, authors investigate the use of adaptive neuro-fuzzy inference system (ANFIS) with extended Kalman filter for fetal ...
DEFF Research Database (Denmark)
Yeboah-Boateng, Ezer Osei
. The elicited experts opinions were used to model the risk function, using neuro-fuzzy techniques, that combines the human inference style and linguistic expressions of fuzzy systems with the learning and parallel processing capabilities of neural networks to analyze the cyber-security vulnerabilities...
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
An intelligent clustering based methodology for confusable diseases ...
African Journals Online (AJOL)
In this paper, an intelligent system driven by fuzzy clustering algorithm and Adaptive Neuro-Fuzzy Inference System for the investigation, diagnosis and management of similar and confusing symptoms of confusable diseases was developed. Data on patients diagnosed and confirmed by laboratory tests of viral hepatitis (H), ...
Hydraulic head and groundwater 111 Cd content interpolations ...
African Journals Online (AJOL)
adaptive neuro-fuzzy inference system (Geo-ANFIS) and empirical Bayesian kriging (EBK) were performed for the alluvium unit of Karabağlar Polje in Muğla, Turkey. Hydraulic head measurements and 111Cd analyses were done for 42 water wells ...
Admissibility of logical inference rules
Rybakov, VV
1997-01-01
The aim of this book is to present the fundamental theoretical results concerning inference rules in deductive formal systems. Primary attention is focused on: admissible or permissible inference rules the derivability of the admissible inference rules the structural completeness of logics the bases for admissible and valid inference rules. There is particular emphasis on propositional non-standard logics (primary, superintuitionistic and modal logics) but general logical consequence relations and classical first-order theories are also considered. The book is basically self-contained and
Z Number Based Fuzzy Inference System for Dynamic Plant Control
Directory of Open Access Journals (Sweden)
Rahib H. Abiyev
2016-01-01
Full Text Available Frequently the reliabilities of the linguistic values of the variables in the rule base are becoming important in the modeling of fuzzy systems. Taking into consideration the reliability degree of the fuzzy values of variables of the rules the design of inference mechanism acquires importance. For this purpose, Z number based fuzzy rules that include constraint and reliability degrees of information are constructed. Fuzzy rule interpolation is presented for designing of an inference engine of fuzzy rule-based system. The mathematical background of the fuzzy inference system based on interpolative mechanism is developed. Based on interpolative inference process Z number based fuzzy controller for control of dynamic plant has been designed. The transient response characteristic of designed controller is compared with the transient response characteristic of the conventional fuzzy controller. The obtained comparative results demonstrate the suitability of designed system in control of dynamic plants.
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.
Causal Inference and Developmental Psychology
Foster, E. Michael
2010-01-01
Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…
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.
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)
van Lith, Pascal; van Lith, P.F.; Betlem, Bernardus H.L.; Roffel, B.
2002-01-01
Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and
Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian
2002-01-01
Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and
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
Mechanical equivalent of Bayesian inference from monitoring data
Cappello, Carlo; Bolognani, Denise; Zonta, Daniele
2016-04-01
Structural health monitoring requires engineers to understand the state of a structure from its observed response. When this information is uncertain, Bayesian probability theory provides a consistent framework for making inference. However, structural engineers are often unenthusiastic about Bayesian logic and prefer to make inference using heuristics. Herein we propose a quantitative method for logical inference based on a formal analogy between linear elastic mechanics and Bayesian inference with Gaussian variables. We start by discussing the estimation of a single parameter under the assumption that all of the uncertain quantities have a Gaussian distribution and that the relationship between the observations and the parameter is linear. With these assumptions, the analogy is stated as follows: the expected value of the considered parameter corresponds to the position of a bar with one degree of freedom and uncertain observations of the parameter are modelled as linear elastic springs placed in series or parallel. If we want to extend the analogy to multiple parameters, we simply have to express the potential energy of the mechanical system associated to the inference problem. The expected value of the parameters is then calculated by minimizing that potential energy. We conclude our contribution by presenting the application of mechanical equivalent to a real-life case study in which we seek the elongation trend of a cable belonging to Adige Bridge, a cable-stayed bridge located North of Trento, Italy.
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
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.
accurate solutions of colebrook- white's friction factor formulae
African Journals Online (AJOL)
HOD
flow friction coefficient using adaptive neuro- fuzzy computing technique. Advances in. Engineering Software Vol. 40, pp 281–287, 2009b. [9] Moody, L.F. Friction factors for pipe flow. Trans. ASME . Vol. 66, pp 671–684, 1944. [10] Moody, L.F. An approximate formula for pipe friction factors. Trans ASME Vol. 69, 1947, pp.
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
2015-02-01
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. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.
Locative inferences in medical texts.
Mayer, P S; Bailey, G H; Mayer, R J; Hillis, A; Dvoracek, J E
1987-06-01
Medical research relies on epidemiological studies conducted on a large set of clinical records that have been collected from physicians recording individual patient observations. These clinical records are recorded for the purpose of individual care of the patient with little consideration for their use by a biostatistician interested in studying a disease over a large population. Natural language processing of clinical records for epidemiological studies must deal with temporal, locative, and conceptual issues. This makes text understanding and data extraction of clinical records an excellent area for applied research. While much has been done in making temporal or conceptual inferences in medical texts, parallel work in locative inferences has not been done. This paper examines the locative inferences as well as the integration of temporal, locative, and conceptual issues in the clinical record understanding domain by presenting an application that utilizes two key concepts in its parsing strategy--a knowledge-based parsing strategy and a minimal lexicon.
Perception, illusions and Bayesian inference.
Nour, Matthew M; Nour, Joseph M
2015-01-01
Descriptive psychopathology makes a distinction between veridical perception and illusory perception. In both cases a perception is tied to a sensory stimulus, but in illusions the perception is of a false object. This article re-examines this distinction in light of new work in theoretical and computational neurobiology, which views all perception as a form of Bayesian statistical inference that combines sensory signals with prior expectations. Bayesian perceptual inference can solve the 'inverse optics' problem of veridical perception and provides a biologically plausible account of a number of illusory phenomena, suggesting that veridical and illusory perceptions are generated by precisely the same inferential mechanisms. © 2015 S. Karger AG, Basel.
Automatic Inference of DATR Theories
Barg, P
1996-01-01
This paper presents an approach for the automatic acquisition of linguistic knowledge from unstructured data. The acquired knowledge is represented in the lexical knowledge representation language DATR. A set of transformation rules that establish inheritance relationships and a default-inference algorithm make up the basis components of the system. Since the overall approach is not restricted to a special domain, the heuristic inference strategy uses criteria to evaluate the quality of a DATR theory, where different domains may require different criteria. The system is applied to the linguistic learning task of German noun inflection.
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.
Wisdom of crowds for robust gene network inference.
Marbach, Daniel; Costello, James C; Küffner, Robert; Vega, Nicole M; Prill, Robert J; Camacho, Diogo M; Allison, Kyle R; Kellis, Manolis; Collins, James J; Stolovitzky, Gustavo
2012-07-15
Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.
Quantifying the multi-scale performance of network inference algorithms.
Oates, Chris J; Amos, Richard; Spencer, Simon E F
2014-10-01
Graphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It is common to assess such algorithms using techniques from classifier analysis. These metrics, based on ability to correctly infer individual edges, possess a number of appealing features including invariance to rank-preserving transformation. However, regulation in biological systems occurs on multiple scales and existing metrics do not take into account the correctness of higher-order network structure. In this paper novel performance scores are presented that share the appealing properties of existing scores, whilst capturing ability to uncover regulation on multiple scales. Theoretical results confirm that performance of a network inference algorithm depends crucially on the scale at which inferences are to be made; in particular strong local performance does not guarantee accurate reconstruction of higher-order topology. Applying these scores to a large corpus of data from the DREAM5 challenge, we undertake a data-driven assessment of estimator performance. We find that the "wisdom of crowds" network, that demonstrated superior local performance in the DREAM5 challenge, is also among the best performing methodologies for inference of regulation on multiple length scales.
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
Type inference for COBOL systems
A. van Deursen (Arie); L.M.F. Moonen (Leon)
1998-01-01
textabstractTypes are a good starting point for various software reengineering tasks. Unfortunately, programs requiring reengineering most desperately are written in languages without an adequate type system (such as COBOL). To solve this problem, we propose a method of automated type inference
Fuel Saving Strategy in Spark Ignition Engine Using Fuzzy Logic Engine Torque Control
Aris Triwiyatno; Sumardi 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...
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
Physics-Based Modeling of Bridge Foundation Scour: Numerical Simulations and Experiments
2013-03-26
with adaptive neuro - fuzzy inference systems , Hydrological Processes, (10 2011): 0. doi: 10.1002/hyp.8228 TOTAL: 12 (b) Papers published in non-peer... fuzzy inference systems , (03 2011) 03/07/2011 9.00 M. Valyrakis, P. Diplas, C. Dancey. Modelling the impulses causing entrainment of coarse grains...Value Theory approach, (03 2011) 03/07/2011 14.00 M. Valyrakis, P. Diplas, C. Dancey. Prediction of coarse pa rticle movement with adaptive neuro
Development of an Intelligent Car Engine Fault Troubleshooting ...
African Journals Online (AJOL)
... to introduce a systematic and intelligent method in car engine troubleshooting and maintenance environments and also provides a troubleshooting framework for other researchers to work on. Keywords: Expert System, knowledge base, troubleshooting, inference engine, knowledge acquisition, artificial intelligence.
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...
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 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.
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.
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.
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...
Bayesian Inference of FRC plasmas
Romero, Jesus A.; Dettrick, Sean; Onofri, Marco; TAE Team
2017-10-01
Bayesian analysis techniques are currently being used at TAE to infer FRC magnetic topology and the radial profile of the electron density. The Bayesian method provides all the solutions compatible with both the prior assumptions and the measurements in the form of a probability distribution termed the posterior, from which the most likely solution and its uncertainty can readily be obtained. Bayesian analysis of field reversed configurations reveals strong field reversal on axis as well as non-monotonic radial density profiles. The later feature is only observed in global transport simulations in cases where significant fast ion pressure and current drive are present. Hence the inferred non-monotonic density profiles are indicative of current drive in the experiment.
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
Applied statistical inference with MINITAB
Lesik, Sally
2009-01-01
Through clear, step-by-step mathematical calculations, Applied Statistical Inference with MINITAB enables students to gain a solid understanding of how to apply statistical techniques using a statistical software program. It focuses on the concepts of confidence intervals, hypothesis testing, validating model assumptions, and power analysis.Illustrates the techniques and methods using MINITABAfter introducing some common terminology, the author explains how to create simple graphs using MINITAB and how to calculate descriptive statistics using both traditional hand computations and MINITAB. Sh
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
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.
Uncertainty estimates for the Bayes Inference Engine, (BIE)
Energy Technology Data Exchange (ETDEWEB)
Beery, Thomas A [Los Alamos National Laboratory
2009-01-01
In the fall 2007 meeting of the BIB users group, two approaches to making uncertainty estimates were presented. Ken Hanson asserted that if the BFGS optimizer was used, the inverse Hessian matrix was the same as the covariance matrix representing parameter uncertainties. John Pang presented preliminary results of a Monte Carlo method called Randomized Maximum Likelihood (RML). The BFGS/Hessian matrix approach may be applied to the region of the 'ideal model' Approximately 250 parameters describing the object density patches that are varied to match an image of 1,000,000 pixels. I cast this in terms of least squares analysis, as it is much better understood. This not as large a conceptual jump as some suppose because many of the functional blocks in the BIB are taken directly from existing least squares programs. If a Gaussian (normal) probability density function is assumed for both the observation and parameter errors, the Bayesian and least squares result should be identical.
Dynamic Protocol Reverse Engineering: A Grammatical Inference Approach
2008-03-01
bottom-up LR (k); and top-down, recur- sive descent and LL(k) parsers . Where the expressive power is: LL(k) ⊂ LR (k) ⊂ Type-2 [224, Vol 1, Chap 3, Sec 6.8...implementation issues that allow deliberately crafted packets which lead a protocol parser to unexpected conditions? 1.3 Related Problem Domains Network...meaning of known signpost values and how the proprietary parser responds to those values [258]. 2.7 Case Studies Here we present an overview of protocol