Fuzzy functions based ARX model and new fuzzy basis function models for nonlinear system identification
Küçük Resim Yok
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Science Bv
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 functionmodels. 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 "MFBF''. 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. (C) 2009 Elsevier B.V. All rights reserved.
Açıklama
Anahtar Kelimeler
Fuzzy functions, ARX modeling, FCM clustering, Fuzzy basis functions, System identification, LSE
Kaynak
Applied Soft Computing
WoS Q Değeri
Q1
Scopus Q Değeri
N/A
Cilt
10
Sayı
2
