Akademik Veri Yönetim Sistemi
Applied Soft Computing Journal, cilt.10, sa.2, ss.439-444, 2010 (SCI-Expanded)
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. © 2009 Elsevier B.V. All rights reserved.