Robust training algorithm of multi-layered neural networks for identification of nonlinear dynamic systems

Author

Song, Q. ; Grimble, M.J.

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

Volume

4

fYear

1997

fDate

4-6 Jun 1997

Firstpage

2419

Abstract

The plant used is a nonlinear difference equation with a bounded deterministic disturbance. Motivated by adaptive control systems a dead zone technique is used for the nonlinear gradient descent algorithm to train multilayered feed-forward neural networks for identification of the nonlinear-dynamic system. The dead zone scheme guarantees the convergence of the neural network in the presence of disturbance. Simulation results are presented to demonstrate robustness of the algorithm

Keywords

conjugate gradient methods; convergence; difference equations; feedforward neural nets; identification; learning (artificial intelligence); multilayer perceptrons; nonlinear differential equations; nonlinear dynamical systems; adaptive control systems; bounded deterministic disturbance; convergence; dead zone technique; disturbance; multilayered feed-forward neural network training; multilayered neural networks; nonlinear difference equation; nonlinear dynamic system identification; nonlinear gradient descent algorithm; robust training algorithm; Adaptive control; Convergence; Difference equations; Feedforward neural networks; Feedforward systems; Multi-layer neural network; Neural networks; Nonlinear dynamical systems; Robustness; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.609162

Filename

609162