Global Asymptotic Stability of Recurrent Neural Networks with Time Varying Delays

Author

Guan, Huanxin ; Zhang, Huaguang ; Wang, Zhanshan ; Liu, Derong

Author_Institution

Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang

fYear

2007

fDate

27-30 May 2007

Firstpage

1005

Lastpage

1008

Abstract

In this paper, two sufficient conditions are established for the global asymptotic stability of recurrent neural networks with multiple time varying delays. The Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality approach are employed in our investigation. Our results are shown to be generalizations of some previously published results and are less conservative than existing results. The present results are also applicable to recurrent neural networks with constant time delays.

Keywords

asymptotic stability; delays; functional equations; linear matrix inequalities; recurrent neural nets; time-varying systems; Lyapunov-Krasovskii stability theory; functional differential equations; global asymptotic stability; linear matrix inequality approach; recurrent neural networks; time varying delays; Associative memory; Asymptotic stability; Delay effects; Eigenvalues and eigenfunctions; Information science; Neural networks; Neurons; Recurrent neural networks; Sufficient conditions; Symmetric matrices;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on

Conference_Location

New Orleans, LA

Print_ISBN

1-4244-0920-9

Electronic_ISBN

1-4244-0921-7

Type

conf

DOI

10.1109/ISCAS.2007.378139

Filename

4252807