LMI approach for stability in stochastic delayed neural systems

Author

Xuyang Lou ; Yong Qiao ; Cui, B.T. ; Ye, Q.

Author_Institution

Key Lab. of Adv. Process Control for Light Ind. (Minist. of Educ.), Jiangnan Univ., Wuxi, China

fYear

2014

fDate

19-21 Aug. 2014

Firstpage

52

Lastpage

57

Abstract

In this paper, the asymptotic stability analysis problem is considered for a class of stochastic Cohen-Grossberg neural networks with time-varying delays. We aim to construct easily verifiable conditions for the asymptotic stability in the mean square of the delayed neural networks. Via a Lyapunov functional and the Halanay inequality technique, several stability criteria are derived. Two examples are provided to illustrate the effectiveness and applicability of the proposed criteria.

Keywords

Lyapunov methods; asymptotic stability; delays; linear matrix inequalities; neural nets; Halanay inequality technique; LMI approach; Lyapunov functional; asymptotic stability analysis problem; linear matrix inequality; mean square; stability criteria; stochastic Cohen-Grossberg neural networks; stochastic delayed neural systems; time-varying delays; Asymptotic stability; Delays; Neural networks; Stability criteria; Stochastic processes; Symmetric matrices; Halanay inequality; Linear matrix inequality; Lyapunov functional; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2014 10th International Conference on

Conference_Location

Xiamen

Print_ISBN

978-1-4799-5150-5

Type

conf

DOI

10.1109/ICNC.2014.6975809

Filename

6975809