System Identification of Nonlinear System using Local Time Delayed Recurrent Neural Network

지역시간지연 순환형 신경회로망을 이용한 비선형 시스템 규명

  • Chong, K.T. ;
  • Hong, D.P.
  • 정길도 (전북대학교 공과대학 제어계측공학과) ;
  • 홍동표 (전북대학교 공과대학 정밀기계공학과)
  • Published : 1995.06.01

Abstract

A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.

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