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    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46080

    題名: 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
    貢獻者: 淡江大學電機工程學系
    關鍵詞: Reinforcement learning;Neural networks;Neuro-fuzzy systems;Classifier systems;Bucket brigade algorithm
    日期: 2006-01
    上傳時間: 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
    顯示於類別:[電機工程學系暨研究所] 期刊論文


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