Fuzzy functions based ARX model and new fuzzy basis function models for nonlinear system identification
Applied Soft Computing Journal, vol.10, no.2, pp.439-444, 2010 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 10 Issue: 2
- Publication Date: 2010
- Doi Number: 10.1016/j.asoc.2009.08.015
- Journal Name: Applied Soft Computing Journal
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.439-444
- Keywords: ARX modeling, FCM clustering, Fuzzy basis functions, Fuzzy functions, LSE, System identification
- Yıldız Technical University Affiliated: No
Abstract
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.