Automatic generation of fuzzy control rules by machine learning methods

Shih Chun Hsu, Jane Yung jen Hsu, I-Jen Chiang

研究成果: 書貢獻/報告類型會議貢獻

16 引文 斯高帕斯(Scopus)

摘要

This paper presents a multi-strategy learning technique for automatic generation of fuzzy control rules. In order to eliminate irrelevant input variables and to prioritize relevant ones according to their influences on the output value(s), the ID3 algorithm is adopted to classify the given set of training I/O data. The resulting decision tree can be easily converted into IF-THEN rules, which are then fuzzified. The fuzzy rules are further improved by tuning the parameters that define their membership functions using the gradient-descent approach. Experimental results of applying the proposed technique to nonlinear system identification have shown improvements over previous work in the area. In addition, it has been successfully applied to mobile robot control in unknown environments.
原文英語
主出版物標題Proceedings - IEEE International Conference on Robotics and Automation
發行者IEEE
頁面287-292
頁數6
1
出版狀態已發佈 - 1995
對外發佈Yes
事件Proceedings of the 1995 IEEE International Conference on Robotics and Automation. Part 1 (of 3) - Nagoya, Jpn
持續時間: 五月 21 1995五月 27 1995

其他

其他Proceedings of the 1995 IEEE International Conference on Robotics and Automation. Part 1 (of 3)
城市Nagoya, Jpn
期間5/21/955/27/95

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering

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  • 引用此

    Hsu, S. C., Hsu, J. Y. J., & Chiang, I-J. (1995). Automatic generation of fuzzy control rules by machine learning methods. 於 Proceedings - IEEE International Conference on Robotics and Automation (卷 1, 頁 287-292). IEEE.