대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)

대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지

GA기반 다항식 뉴럴네트워크를 이용한 비선형 모델링

Nonlinear modeling by means of Ga based Polynomial Neural Networks

  • 김동원 (원광대학교 공과대학 제어계측공학과) ;
  • 노석범 (원광대학교 공과대학 제어계측공학과) ;
  • 이동윤 (원광대학교 공과대학 제어계측공학과) ;
  • 오성권 (원광대학교 공과대학 제어계측공학과)
  • Kim, Dong-Won (School of Electrical and Electronic Engineering, Wonkwang Univ.) ;
  • Roh, Seok-Beom (School of Electrical and Electronic Engineering, Wonkwang Univ.) ;
  • Lee, Dong-Yoon (School of Electrical and Electronic Engineering, Wonkwang Univ.) ;
  • Oh, Sung-Kwun (School of Electrical and Electronic Engineering, Wonkwang Univ.)
  • 발행 : 2001.11.24
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초록

In this paper, Polynomial Neural Networks(PNN) is proposed to overcome some problems, such as the conflict between overfitting and good generation, and low reliability and to control nonlinearity and unknown parameter of complex system. PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be generated according to the system environments. The performances depend on two factors, number of inputs and order of polynomials in each node directly. In most cases these factors are decided by the trial and error of designer so optimization is needed in deciding procedure of the factors. Evolutionary algorithm is applied to decide the factors in PNN. The study is illustrated with the aid of representative time series data for gas furnace process used widely for performance comparison, and shows the designed PNN architecture with evolutionary algorithm.

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