Global asymptotic stability for a class of nonsymmetric neural networks

Author

Forti, M. ; Liberatore, A. ; Manetti, S. ; Marini, M.

Author_Institution

Dept. of Electron. Eng., Florence Univ., Firenze, Italy

fYear

1993

fDate

3-6 May 1993

Firstpage

2580

Abstract

The authors show that the property of global asymptotic stability is guaranteed for a class of neural circuits with a special form of nonsymmetric interconnection matrix. They also show that neural networks used to solve typical optimization problems such as linear and quadratic programming problems fall into the class of circuits studied here and are characterized by a unique globally asymptotically stable equilibrium. The results are proved by means of the Lyapunov method and by finding suitable Lyapunov functions that are valid for special classes of neural networks with nonsymmetric interconnection matrices as described

Keywords

Lyapunov methods; asymptotic stability; linear programming; neural nets; quadratic programming; Lyapunov functions; Lyapunov method; global asymptotic stability; linear programming; nonsymmetric interconnection matrix; nonsymmetric neural networks; quadratic programming; Artificial neural networks; Asymptotic stability; Automatic control; Hopfield neural networks; Integrated circuit interconnections; Lyapunov method; Neural networks; Neurons; Quadratic programming; Symmetric matrices;

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.1993.394293

Filename

394293