대한전기학회논문지:시스템및제어부문D (The Transactions of the Korean Institute of Electrical Engineers D)

대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지

진화론적 최적 뉴로퍼지 네트워크： 해석과 설계

Genetically Optimized Neurofuzzy Networks: Analysis and Design

  • 박병준 (원광대학 전기전자ㆍ정보공학부) ;
  • 김현기 (수원대학 전기공학) ;
  • 오성권 (원광대학 전기전자ㆍ정보공학부)
  • 발행 : 2004.08.01
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초록

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

키워드

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