Adaptive fuzzy approach to function approximation with PSO and RLSE

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題名:	Adaptive fuzzy approach to function approximation with PSO and RLSE
作者:	Li,CS;Wu,TH
貢獻者:	資訊管理學系
關鍵詞:	UNIVERSAL APPROXIMATORS;SYSTEMS
日期:	2011
上傳時間:	2012-03-27 19:07:06 (UTC+8)
出版者:	國立中央大學
摘要:	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.
關聯:	EXPERT SYSTEMS WITH APPLICATIONS
顯示於類別:	[資訊管理學系] 期刊論文

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