Stability analysis of dynamical neural networks

Author

Fang, Yuguang ; Kincaid, Thomas G.

Author_Institution

Dept. of Electr. Comput. & Syst. Eng., Boston Univ., MA, USA

Volume

7

Issue

4

fYear

1996

fDate

7/1/1996 12:00:00 AM

Firstpage

996

Lastpage

1006

Abstract

In this paper, we use the matrix measure technique to study the stability of dynamical neural networks. Testable conditions for global exponential stability of nonlinear dynamical systems and dynamical neural networks are given. It shows how a few well-known results can be unified and generalized in a straightforward way. Local exponential stability of a class of dynamical neural networks is also studied; we point out that the local exponential stability of any equilibrium point of dynamical neural networks is equivalent to the stability of the linearized system around that equilibrium point. From this, some well-known and new sufficient conditions for local exponential stability of neural networks are obtained

Keywords

Hopfield neural nets; linearisation techniques; matrix algebra; nonlinear dynamical systems; stability; Hopfield type neural networks; dynamical neural networks; equilibrium point; local exponential stability; matrix measure; nonlinear dynamical systems; sufficient conditions; Circuit stability; Differential equations; Integrated circuit interconnections; Linear matrix inequalities; Linear systems; Neural networks; Nonlinear dynamical systems; Stability analysis; Sufficient conditions; Vectors;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.508941

Filename

508941