Title :
Continuous-time recurrent multilayer perceptrons for nonlinear system identification
Author :
Yu, Wen ; Li, XiaoOu
Author_Institution :
Departamento de Control Automatico, CINVESTAV-IPN, Mexico
Abstract :
In this paper continuous-time recurrent multilayer perceptrons (RMLP) are proposed to identify nonlinear systems. Using the function approximation theorem for multilayer perceptrons(MLP), we conclude that RMLP can approximate any dynamic system in any degree of accuracy. By means of a Lyapunov-like analysis, a stable learning algorithm for RMLP is determined. The suggested learning algorithm is similar to the well-known backpropagation rule of the multilayer perceptrons but with an additional term which assure the stability of identification error
Keywords :
Lyapunov methods; backpropagation; continuous time systems; function approximation; identification; multilayer perceptrons; nonlinear systems; stability; Lyapunov-like analysis; backpropagation rule; continuous-time recurrent multilayer perceptron; dynamic system; function approximation theorem; identification error; learning algorithm; nonlinear system identification; Backpropagation algorithms; Function approximation; Least squares approximation; Multilayer perceptrons; Neural networks; Nonhomogeneous media; Nonlinear dynamical systems; Nonlinear systems; Recurrent neural networks; Stability;
Conference_Titel :
Control Applications, 2005. CCA 2005. Proceedings of 2005 IEEE Conference on
Conference_Location :
Toronto, Ont.
Print_ISBN :
0-7803-9354-6
DOI :
10.1109/CCA.2005.1507367
