혼돈 시계열의 예측을 위한 Radial Basis 함수 회로망 설계

Radial basis function network design for chaotic time series prediction

  • 신창용 (연세대학교 공대 전기공학과) ;
  • 김택수 (연세대학교 공대 전기공학과) ;
  • 최윤호 (경기대학교 공대 전기공학과) ;
  • 박상희 (연세대학교 공대 전기공학과)
  • 발행 : 1996.04.01

초록

In this paper, radial basis function networks with two hidden layers, which employ the K-means clustering method and the hierarchical training, are proposed for improving the short-term predictability of chaotic time series. Furthermore the recursive training method of radial basis function network using the recursive modified Gram-Schmidt algorithm is proposed for the purpose. In addition, the radial basis function networks trained by the proposed training methods are compared with the X.D. He A Lapedes's model and the radial basis function network by nonrecursive training method. Through this comparison, an improved radial basis function network for predicting chaotic time series is presented. (author). 17 refs., 8 figs., 3 tabs.

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