Identification of nonlinear dynamical systems based on self-organized distributed networks

자율분산 신경망을 이용한 비선형 동적 시스템 식별

  • 최종수 (POSCON 기술연구소) ;
  • 김형석 (전북대 공대 제어계측공학과) ;
  • 김성중 (전북대 공대 제어계측공학과) ;
  • 권오신 (군산대 공대 제어계측공학과) ;
  • 김종만 (전북대 대학원 전기공학과)
  • Published : 1996.04.01


The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Networks(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism. Each local network learns only data in a subregion. This paper also discusses neural network as identifier of nonlinear dynamical systems. The structure of nonlinear system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems. (author). 13 refs., 7 figs., 2 tabs.