Automatic generation of fuzzy control rules by machine learning methods

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
PublisherIEEE
Pages287-292
Number of pages6
Volume1
Publication statusPublished - 1995
Externally publishedYes
EventProceedings of the 1995 IEEE International Conference on Robotics and Automation. Part 1 (of 3) - Nagoya, Jpn
Duration: May 21 1995May 27 1995

Other

OtherProceedings of the 1995 IEEE International Conference on Robotics and Automation. Part 1 (of 3)
CityNagoya, Jpn
Period5/21/955/27/95

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering

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  • Cite this

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