Recognition of stability domain of nonlinear dynamical system using multilayer feed-forward artificial neural network approach

Author

Marpaka, D.R.

Author_Institution

Dept. of Electr. Eng., Tennessee State Univ., Nashville, TN, USA

fYear

1991

fDate

10-12 Mar 1991

Firstpage

212

Lastpage

217

Abstract

A method for obtaining the stability region of a nonlinear dynamical system using an artificial neural network is presented. This method takes advantage of many properties of adaptive networks. A design philosophy is presented. A Rumelhart feed-forward neural network with backward propagation of error algorithm is used to update the synaptic weights and thresholds until the desired stability region is recognized. An example of a nonlinear system is presented to illustrate the method

Keywords

control system analysis; neural nets; nonlinear control systems; stability criteria; Rumelhart feed-forward neural network; back-propagation; backward propagation; multilayer feed-forward artificial neural network; nonlinear dynamical system; stability domain recognition; synaptic weight updating; threshold updating; Artificial neural networks; Computer networks; Feedforward systems; Lyapunov method; Multi-layer neural network; Nonhomogeneous media; Nonlinear dynamical systems; Nonlinear systems; Stability; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, 1991. Proceedings., Twenty-Third Southeastern Symposium on

Conference_Location

Columbia, SC

ISSN

0094-2898

Print_ISBN

0-8186-2190-7

Type

conf

DOI

10.1109/SSST.1991.138550

Filename

138550