Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Neuro-Fuzzy Modeling based on Self-Organizing Clustering

자기구성 클러스터링 기반 뉴로-퍼지 모델링

  • Kim Sung-Suk (Chungbuk National University School of Electrical and Electronic Engineering Research Institute for Computer and Information , Communication) ;
  • Ryu Jeong-Woong (Chungbuk National University School of Electrical and Electronic Engineering Research Institute for Computer and Information , Communication) ;
  • Kim Yong-Tae (Department of Information & Control Engineering, Hankyong National University)
  • 김승석 (충북대학교 전기전자공학부, 컴퓨터 정보통신 연구소) ;
  • 유정웅 (충북대학교 전기전자공학부, 컴퓨터 정보통신 연구소) ;
  • 김용태 (한경대학교 정보제어공학과, 전자기술종합연구소)
  • Published : 2005.12.01
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Abstract

In this Paper, we Propose a new neuro-fuzzy modeling using clustering-based learning method. In the proposed clustering method, number of clusters is automatically inferred and its parameters are optimized simultaneously, Also, a neuro-fuzzy model is learned based on clustering information at same time. In the previous modelling method, clustering and model learning are performed independently and have no exchange of its informations. However, in the proposed method, overall neuro-fuzzy model is generated by using both clustering and model learning, and the information of modelling output is used to clustering of input. The proposed method improve the computational load of modeling using Subtractive clustering method. Simulation results show that the proposed method has an effectiveness compared with the previous methods.

본 논문에서는 클러스터링을 뉴로-퍼지 모델에 직접 적용하여 모델을 최적화하는 방법을 제안하였다. 기존의 오차미분기반 학습을 통한 뉴로-퍼지 모델의 최적화 과정과는 달리 제안된 방법은 클러스터링 학습과 연계하여 모델을 구성하며 자율적으로 클러스터의 수를 추정하며 동시에 최적화를 수행한다. 순차적인 학습 기법에서는 각각의 학습 기법을 따로 적용하여 모델링을 실시하였으나 제안된 기법에서는 하나의 클러스터링 학습으로 전체 모델의 학습을 실시하였다. 또한 제안된 방법에서는 클러스터링이 수렴하는 만큼 전체 모델의 연산량이 감소하여 학습과정에서 발생하는 연산량 문제를 개선하였다. 시뮬레이션을 통하여 기존의 연구 결과들과 비교하여 제안된 기법의 유용성을 보였다.

Keywords

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