Passivity analysis for dynamic multilayer neuro identifier

Author

Yu, Wen

Author_Institution

Departamento de Control Automatico, CINVESTAV-IPN, Mexico City, Mexico

Volume

50

Issue

1

fYear

2003

Firstpage

173

Lastpage

178

Abstract

In this work, dynamic multilayer neural networks are used for nonlinear system online identification. The passivity approach is applied to access several stability properties of the neuro identifier. The conditions for passivity, stability, asymptotic stability, and input-to-state stability are established. We conclude that the commonly-used backpropagation algorithm with a modification term which is determined by offline learning may make the neuro identification algorithm robustly stable with respect to any bounded uncertainty.

Keywords

backpropagation; identification; matrix algebra; neural nets; stability; asymptotic stability; backpropagation algorithm; bounded uncertainty; dynamic multilayer neural networks; input-to-state stability; modification term; neuro identification algorithm; neuro identifier; nonlinear system online identification; offline learning; passivity analysis; stability properties; Asymptotic stability; Backpropagation algorithms; Circuit stability; Multi-layer neural network; Neural networks; Nonhomogeneous media; Nonlinear dynamical systems; Nonlinear systems; Stability analysis; Vehicle dynamics;

fLanguage

English

Journal_Title

Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on

Publisher

ieee

ISSN

1057-7122

Type

jour

DOI

10.1109/TCSI.2002.807519

Filename

1179165