Persistence of equilibria under weight variation of feedback continuous-time neural network

Author

Ling, Bo ; Salam, Fathi M A

Author_Institution

Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI, USA

fYear

1993

fDate

3-6 May 1993

Firstpage

2160

Abstract

For binary patterns, the authors consider the variation of equilibria of the Hopfield-type feedback continuous-time neural network due to perturbations. They show that the equilibria of the feedback continuous-time neural network and its perturbed network are very close as long as the variation of weights is relatively small. The variation of the equilibria can be estimated given the upper bound of the variation of weights

Keywords

Hopfield neural nets; continuous time systems; learning (artificial intelligence); pattern recognition; Hopfield-type feedback neural net; binary patterns; equilibria variation; feedback continuous-time neural network; perturbations; perturbed network; upper bound; weight variation; Artificial neural networks; Bifurcation; Circuits; Feedforward neural networks; Hardware; Hopfield neural networks; Neural networks; Neurofeedback; Stability; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1993., ISCAS '93, 1993 IEEE International Symposium on

Conference_Location

Chicago, IL

Print_ISBN

0-7803-1281-3

Type

conf

DOI

10.1109/ISCAS.1993.394186

Filename

394186

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2612601