An Interactively Recurrent Functional Neural Fuzzy Network with Fuzzy Differential Evolution and its Applications

Please use this identifier to cite or link to this item: http://ir.lib.ncut.edu.tw/handle/987654321/5963

Title:	An Interactively Recurrent Functional Neural Fuzzy Network with Fuzzy Differential Evolution and its Applications
Authors:	CHENG-JIAN LIN, CHIH-FENG WU,HSUEH-YI LIN & CHENG-YI YU
Contributors:	圖書館
Keywords:	Control differential evolution neural fuzzy network prediction recurrent network
Date:	2015
Issue Date:	2016-10-20 14:48:35 (UTC+8)
Abstract:	In this paper, an interactively recurrent functional neural fuzzy network (IRFNFN) with fuzzy differential evolution (FDE) learning method was proposed for solving the control and the prediction problems. The traditional differential evolution (DE) method easily gets trapped in a local optimum during the learning process, but the proposed fuzzy differential evolution algorithm can overcome this shortcoming. Through the information sharing of nodes in the interactive layer, the proposed IRFNFN can effectively reduce the number of required rule nodes and improve the overall performance of the network. Finally, the IRFNFN model and associated FDE learning algorithm were applied to the control system of the water bath temperature and the forecast of the sunspot number. The experimental results demonstrate the effectiveness of the proposed method.
Relation:	Sains Malaysiana 44(12)(2015): 1721–1728
Appears in Collections:	[Department of Computer Science and Information Engineering] 【資訊工程系所】期刊論文

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