Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A course in fuzzy systems and control
A course in fuzzy systems and control
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Fuzzy functions and their fundamental properties
Fuzzy Sets and Systems
Cluster Analysis for Data Mining and System Identification
Cluster Analysis for Data Mining and System Identification
On-line system identification of complex systems using Chebyshev neural networks
Applied Soft Computing
A new recurrent neurofuzzy network for identification of dynamic systems
Fuzzy Sets and Systems
Improving the prediction and parsimony of ARX models using multiscale estimation
Applied Soft Computing
Applied Soft Computing
Fuzzy rule-base driven orthogonal approximation
Neural Computing and Applications
A FCM-based deterministic forecasting model for fuzzy time series
Computers & Mathematics with Applications
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Hybrid-fuzzy modeling and identification
Applied Soft Computing
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In this study, auto regressive with exogenous input (ARX) modeling is improved with fuzzy functions concept (FF-ARX). Fuzzy function with least squares estimation (FF-LSE) method has been recently developed and widely used with a small improvement with respect to least squares estimation method (LSE). FF-LSE is structured with only inputs and their membership values. This proposed model aims to increase the capability of the FF-LSE by widening the regression matrix with lagged input-output values. In addition, by using same idea, we proposed also two new fuzzy basis function models. In the first, basis of the fuzzy system and lagged input-output values are structured together in the regression matrix and named as ''L-FBF''. Secondly, instead of using basis function, the membership values of the lagged input-output values are used in the regression matrix by using Gaussian membership functions, called ''M-FBF''. Therefore, the power of the fuzzy basis function is also enhanced. For the corresponding models, antecedent part parameters for the input vectors are determined with fuzzy c-means (FCM) clustering algorithm. The consequent parameters of the all models are estimated with the LSE. The proposed models are utilized and compared for the identification of nonlinear benchmark problems.