| Title |
Identification of nonlinear dynamical systems based on self-organized distributed networks |
| Authors |
최종수 ; 김형석 ; 김성중 ; 권오신 ; 김종만 |
| Keywords |
Nonlinear Dynamical Systems ; System Identification ; Self-Organized Distributed Networks(SODN) ; Multilayer Neural Networks(MNN) |
| Abstract |
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. |