Adaptive fuzzy approach to function approximation with PSO and RLSE

NCUIR > School of Management at National Central University > Department of Information Management > journal & Dissertation > Item 987654321/51823

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51823

Title:	Adaptive fuzzy approach to function approximation with PSO and RLSE
Authors:	Li,CS;Wu,TH
Contributors:	資訊管理學系
Keywords:	UNIVERSAL APPROXIMATORS;SYSTEMS
Date:	2011
Issue Date:	2012-03-27 19:07:06 (UTC+8)
Publisher:	國立中央大學
Abstract:	A new adaptive fuzzy approach to function approximation is proposed in the paper. A Takagi-Sugeno (T-S) type fuzzy system is used as the function approximator in the study. The proposed approach uses a hybrid learning method to train the T-S fuzzy system to achieve high accuracy in function approximation. The hybrid learning method combines both the particle swarm optimization (PSO) and the recursive least squares estimator (RLSE) to update the parameters of the fuzzy approximator. The PSO is used to update the premise part of the fuzzy system while the consequent part is updated by the RLSE. The PSO-RLSE learning method is very efficient in learning convergence. The proposed approach is compared to other methods. Three benchmark functions are used for the performance comparison. The proposed approach shows superior performance to compared approaches, in terms of approximation accuracy and learning convergence. (C) 2011 Elsevier Ltd. All rights reserved.
Relation:	EXPERT SYSTEMS WITH APPLICATIONS
Appears in Collections:	[Department of Information Management] journal & Dissertation

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