Application of neural networks incorporated with real-valued genetic algorithms in knowledge acquisition

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/46192

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題名:	Application of neural networks incorporated with real-valued genetic algorithms in knowledge acquisition
作者:	蘇木春;Su, Mu-chun;Chang, Hsiao-te
貢獻者:	淡江大學電機工程學系
關鍵詞:	Neural networks;Pattern recognition;Medicine;Fuzzy control
日期:	2000-05-01
上傳時間:	2010-03-26 21:11:11 (UTC+8)
出版者:	Elsevier
摘要:	Often a major difficulty in the design of rule-based systems is the process of acquiring the requisite knowledge in the form of If–Then rules. This paper presents a class of fuzzy degraded hyperellipsoidal composite neural networks (FDHECNNs) that are trained to provide appealing solutions to the problem of knowledge acquisition. The values of the network parameters, after sufficient training, are then utilized to generate If–Then rules on the basis of preselected meaningful features. In order to avoid the risk of getting stuck in local minima during the training process, a real-valued genetic algorithm is proposed to train FDHECNNs. The effectiveness of the method is demonstrated on two problems, namely, the “truck backer-upper” problem as well as real-world application of a hypothesis regarding the pathophysiology of diabetes.
關聯:	Fuzzy sets and systems 112(1), pp.85-97
DOI:	10.1016/S0165-0114(98)00180-8
顯示於類別:	[電機工程學系暨研究所] 期刊論文

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