Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A New Identification Method for a Fuzzy Model

퍼지모델의 새로운 설정 방법

  • 박민기 (연세대학교 전자공학과) ;
  • 지승환 (연세대학교 전자공학과) ;
  • 박민용 (연세대학교 전자공학과)
  • Published : 1995.06.01
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Abstract

The identification of a fuzzy model using input-output data consists of two parts :Structure identification and parameter identification. In this paper an algorithm to identify those parameters and structures is suggested to solve the problems of the conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, each of which considers the linearity and continuity respectively. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation, where we only consider a single input and single output system.

입출력 데이터를 이용한 퍼지모델의 설정은 구조 설정과 변수 설정으로 나누어진다. 본 논문에서는 기존 방법의 문제점을 해결하고 퍼지모델의 이러한 구조와 변수를 설정하는 새로운 방법을 제안한다. 입출력 데이터가 주어지면, 후건부 변수는 선형성과연속성을 고려하여 휴(Hough) 변환과클러스터링 방법에 의해 각각 설정된다. 또한 경사 하강법(Gradient descent method)을 사용하여 퍼지모델 변수의 미세조정을 행한다. 마지막으로 단일 입출력 시스템에 대하여 시뮬레이션을 통해 제안된 방법의 유효성을 보인다.

Keywords

References

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