한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제12권9호
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  • Pages.1591-1598
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  • 2008
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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학습기능을 이용한 Z. Cao의 퍼지추론방식

Z. Cao's Fuzzy Reasoning Method using Learning Ability

  • 박진현 (진주산업대학교 메카트로닉스공학과) ;
  • 이태환 (진주산업대학교 메카트로닉스공학과) ;
  • 최영규 (부산대학교 전자전기정보컴퓨터공학부)
  • 발행 : 2008.09.30
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초록

과거 Z. Cao는 Relation matrix를 사용한 정밀한 추론이 가능한 NFRM(New fuzzy reasoning method)을 제안하였다. 이는 추론의 규칙 수가 적음에도 불구하고 Mamdani의 퍼지추론방식에 비하여 좋은 성능을 보였다. 그러나 정밀한 추론을 위하여 relation maoix는 시행착오법을 사용하여 구하고, 이는 많은 시간과 노력이 필요하다. 본 연구에서는 이러한 relation matrix를 구하기 위하여 시행착오법에 의해 소요되는 많은 시간과 노력을 줄이고, 더욱 정밀한 추론 성능의 개선을 위하여 경사감소학습법을 사용한 학습기능을 갖는 Z. Cao의 퍼지추론 방식을 제안하고자 한다. 모의실험은 비선형 시스템에 적용하여 제안된 추론방식이 좋은 성능을 나타냄을 보였다.

Z. Cao had proposed NFRM(new fuzzy reasoning method) which infers in detail using relation matrix. In spite of the small inference rules, it shows good performance than mamdani's fuzzy inference method. In this paper, we propose Z. Cao's fuzzy inference method with learning ability which is used a gradient descent method in order to improve the performances. It is hard to determine the relation matrix elements by trial and error method. Because this method is needed many hours and effort. Simulation results are applied nonlinear systems show that the proposed inference method using a gradient descent method has good performances.

키워드

참고문헌

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