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A Learning Algorithm for Optimal Fuzzy Control Rules

최적의 퍼지제어규칙을 얻기위한 퍼지학습법

  • 정병묵 (영남대학교 기계공학과)
  • Published : 1996.02.01

Abstract

A fuzzy learning algorithm to get the optimal fuzzy rules is presented in this paper. The algorithm introduces a reference model to generate a desired output and a performance index funtion instead of the performance index table. The performance index funtion is a cost function based on the error and error-rate between the reference and plant output. The cost function is minimized by a gradient method and the control input is also updated. In this case, the control rules which generate the desired response can be obtained by changing the portion of the error-rate in the cost funtion. In SISO(Single-Input Single- Output)plant, only by the learning delay, it is possible to experss the plant model and to get the desired control rules. In the long run, this algorithm gives us the good control rules with a minimal amount of prior informaiton about the environment.

Keywords

Fuzzy Control;Learning Control;Optimal Control;Model Reference Fuzzy Control;Regulation Control

References

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