A new approach to fuzzy classifier systems and its application in self-generating neuro-fuzzy systems

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 987654321/46080

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题名:	A new approach to fuzzy classifier systems and its application in self-generating neuro-fuzzy systems
作者:	Su, Mu-chun;Chou, Chien-hsing;賴友仁;Lai, Eugene;Lee, Jonathan
贡献者:	淡江大學電機工程學系
关键词:	Reinforcement learning;Neural networks;Neuro-fuzzy systems;Classifier systems;Bucket brigade algorithm
日期:	2006-01
上传时间:	2010-03-26 20:53:58 (UTC+8)
出版者:	Elsevier
摘要:	A classifier system is a machine learning system that learns syntactically simple string rules (called classifiers) through a genetic algorithm to guide its performance in an arbitrary environment. In a classifier system, the bucket brigade algorithm is used to solve the problem of credit assignment, which is a critical problem in the field of reinforcement learning. In this paper, we propose a new approach to fuzzy classifier systems and a neuro-fuzzy system referred to as ACSNFIS to implement the proposed fuzzy classifier system. The proposed system is tested by the balancing problem of a cart pole and the back-driving problem of a truck to demonstrate its performance.
關聯:	Neurocomputing 69(4), pp.586-614
DOI:	10.1016/j.neucom.2004.11.033
显示于类别:	[電機工程學系暨研究所] 期刊論文

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