Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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The Design of Adaptive Fuzzy Polynomial Neural Networks Architectures Based on Fuzzy Neural Networks and Self-Organizing Networks

퍼지뉴럴 네트워크와 자기구성 네트워크에 기초한 적응 퍼지 다항식 뉴럴네트워크 구조의 설계

  • Park, Byeong-Jun (Dept.of Electric Electronics Information Engineering, Wonkwang University) ;
  • Oh, Sung-Kwun (Dept.of Electric Electronics Information Engineering, Wonkwang University) ;
  • Jang, Sung-Whan (Dept.of Electric Electronics Information Engineering, Wonkwang University)
  • 박병준 (원광대학교 전기전자 및 정보공학부) ;
  • 오성권 (원광대학교 전기전자 및 정보공학부) ;
  • 장성환 (원광대학교 전기전자 및 정보공학부)
  • Published : 2002.02.01
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Abstract

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

Keywords

References

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