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

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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A New Modeling Approach to Fuzzy-Neural Networks Architecture

퍼지 뉴럴 네트워크 구조로의 새로운 모델링 연구

  • Park, Ho-Sung (Dept.of Control Instrumentation Engineering, Wonkwang University) ;
  • Oh, Sung-Kwun (Dept.of Electric Electronics Information Engineering, Wonkwang University) ;
  • Yoon, Yang-Woung (Dept.of Electric Electronics Information Engineering, Wonkwang University)
  • 박호성 (원광대학교 제어계측공학과) ;
  • 오성권 (원광대학교 전기전자 및 정보공학부) ;
  • 윤양웅 (원광대학교 전기전자 및 정보공학부)
  • Published : 2001.08.01
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Abstract

In this paper, as a new category of fuzzy-neural networks architecture, we propose Fuzzy Polynomial Neural Networks (FPNN) and discuss a comprehensive design methodology related to its architecture. FPNN dwells on the ideas of fuzzy rule-based computing and neural networks. The FPNN architecture consists of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as Fuzzy Polynomial Neuron(FPN). The conclusion part of the rules, especially the regression polynomial, uses several types of high-order polynomials such as linear, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. It is worth stressing that the number of the layers and the nods in each layer of the FPNN are not predetermined, unlike in the case of the popular multilayer perceptron structure, but these are generated in a dynamic manner. With the aid of two representative time series process data, a detailed design procedure is discussed, and the stability is introduced as a measure of stability of the model for the comparative analysis of various architectures.

Keywords

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