대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)

대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지

진화론적 퍼지 다항식 뉴럴 네트워크를 이용한 소프트웨어 공정의 최적 모델 설계

Optimal Model Design of Software Process Using Genetically Fuzzy Polynomial Neyral Network

  • 이인태 (수원대학교 공과대학 전기공학과) ;
  • 오성권 (수원대학교 공과대학 전기공학과) ;
  • 김현기 (수원대학교 공과대학 전기공학과)
  • Lee, In-Tae (Department of Electrical Engineering, University of Suwon) ;
  • Oh, Sung-Kwun (Department of Electrical Engineering, University of Suwon) ;
  • Kim, Hyun-Ki (Department of Electrical Engineering, University of Suwon)
  • 발행 : 2005.07.18
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초록

The optimal structure of the conventional Fuzzy Polynomial Neural Networks (FPNN)[3] depends on experience of designer. For the conventional Fuzzy Polynomial Neural Networks, input variable number, number of input variable, number of Membership Functions(MFs) and consequence structures are selected through the experience of a model designer iteratively. In this paper, we propose the new design methodology to find the optimal structure of Fuzzy Polymomial Neural Network by using Genetic Algorithms(GAs)[4, 5]. In the sequel, It is shown that the proposed Advanced Genetic Algorithms based Fuzzy Polynomial Neural Network(Advanced GAs-based FPNN) is more useful and effective than the existing models for nonlinear process. We used Medical Imaging System(MIS)[6] data to evaluate the performance of the proposed model.

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