| 標題：A new approach to fuzzy classifier systems and its application in self-generating neuro-fuzzy systems |
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| 學年 | 94 |
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| 學期 | 1 |
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| 出版（發表）日期 | 2006/01/01 |
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| 作品名稱 | A new approach to fuzzy classifier systems and its application in self-generating neuro-fuzzy systems |
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| 作品名稱（其他語言） | |
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| 著者 | Su, Mu-chun; Chou, Chien-hsing; 賴友仁; Lai, Eugene; Lee, Jonathan |
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| 單位 | 淡江大學電機工程學系 |
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| 出版者 | Elsevier |
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| 著錄名稱、卷期、頁數 | Neurocomputing 69(4), pp.586-614 |
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| 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. |
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| 關鍵字 | Reinforcement learning;Neural networks;Neuro-fuzzy systems;Classifier systems;Bucket brigade algorithm |
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| 語言 | 英文 |
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| ISSN | 0925-2312 |
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| 期刊性質 | 國外 |
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| 收錄於 | |
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| 產學合作 | |
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| 通訊作者 | |
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| 審稿制度 | 否 |
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| 國別 | 荷蘭 |
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| 公開徵稿 | |
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| 出版型式 | ,電子版,紙本 |
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| 相關連結 | |
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| SDGs | |
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