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

NCU Institutional Repository > 管理學院 > 企業管理學系 > 期刊論文 > Item 987654321/51710

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/51710

题名:	Non-uniform self-selective coder for fuzzy rules and its application
作者:	Yen,ECC
贡献者:	企業管理學系
关键词:	NEURAL-NETWORK
日期:	2010
上传时间:	2012-03-27 19:03:22 (UTC+8)
出版者:	國立中央大學
摘要:	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.
關聯:	EXPERT SYSTEMS WITH APPLICATIONS
显示于类别:	[企業管理學系] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	340	检视/开启

在NCUIR中所有的数据项都受到原著作权保护.

社群 sharing