Non-uniform self-selective coder for fuzzy rules and its application

NCUIR > School of Management at National Central University > Department of Business Administration > journal & Dissertation > Item 987654321/51710

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51710

Title:	Non-uniform self-selective coder for fuzzy rules and its application
Authors:	Yen,ECC
Contributors:	企業管理學系
Keywords:	NEURAL-NETWORK
Date:	2010
Issue Date:	2012-03-27 19:03:22 (UTC+8)
Publisher:	國立中央大學
Abstract:	We reconsider the application scope of fuzzy rules and find the following. (1) Using spline, we can easily obtain more accurate results than those obtain by the generalized dynamic fuzzy neural network. (2) If the model is nonlinear with a disturbance term, we obtain that the checking error is very large even though the training error is small. If the model is chaotic with a disturbance term, we obtain that both the training error and checking error are very large. (3) Using a sequential algorithm as in the generalized dynamic fuzzy neural network, we would always be trapped at the local minima rather than the global minimum. Therefore we use the non-uniform self-selective coder instead and show how it works by an empirical example. (C) 2009 Elsevier Ltd. All rights reserved.
Relation:	EXPERT SYSTEMS WITH APPLICATIONS
Appears in Collections:	[Department of Business Administration ] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	342	View/Open

社群 sharing