대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)

대한전기학회 (The Korean Institute of Electrical Engineers)
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메시 유전알고리듬을 이용한 퍼지모델링 방법

Fuzzy Modeling Schemes Using Messy Genetic Algorithms

  • 권오국 (연세대학교 전기공학과) ;
  • 장욱 (연세대학교 전기공학과) ;
  • 주영훈 (군산대학교 제어계측공학과) ;
  • 박진배 (연세대학교 전기공학과)
  • Kwon, Oh-Kook (Dept. of Electrical Engineering, Yonsei University) ;
  • Chang, Wook (Dept. of Electrical Engineering, Yonsei University) ;
  • Joo, Young-Hoon (Dept. of Control and Instrumentation, Kunsan National University) ;
  • Park, Jin-Bae (Dept. of Electrical Engineering, Yonsei University)
  • 발행 : 1998.07.20
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초록

Fuzzy inference systems have found many applications in recent years. The fuzzy inference system design procedure is related to an expert or a skilled human operator in many fields. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. The messy genetic algorithm is used to obtain structurally optimized fuzzy neural network models. Structural optimization is regarded important before neural network based learning is switched into. We have applied the method to the problem of a time series estimation.

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