Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control

Chi Hsu Wang*, Wei Yen Wang, Tsu Tian Lee, Pao Shun Tseng

*此作品的通信作者

研究成果: 雜誌貢獻會議論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

A general methodology for constructing fuzzy membership functions via B-spline curve is proposed. By using the method of least-squares, we translate the empirical data into the form of the control points of B-spline curves to construct fuzzy membership functions. This unified form of fuzzy membership functions is called as B-spline membership functions (BMF's). By using the local control property of B-spline curve, the BMF's can be tuned locally during learning process. For the control of a model car through fuzzy-neural networks, it is shown that the local tuning of BMF's can indeed reduce the number of iterations tremendously.

原文英語
頁(從 - 到)2008-2014
頁數7
期刊Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
2
出版狀態已發佈 - 1994
對外發佈
事件Proceedings of the 1994 IEEE International Conference on Systems, Man and Cybernetics. Part 1 (of 3) - San Antonio, TX, USA
持續時間: 1994 10月 21994 10月 5

ASJC Scopus subject areas

  • 控制與系統工程
  • 硬體和架構

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