Global exponential robust stability of stochastic reaction-diffusion static neural networks with mixed time delays

Author

Chunge Lu ; Linshan Wang

Author_Institution

Coll. of Math. Sci., Ocean Univ. of China, Qingdao, China

fYear

2013

fDate

23-25 July 2013

Firstpage

13

Lastpage

17

Abstract

In this paper, a class of stochastic reaction-diffusion static neural networks with mixed time delays is investigated. Lyapunov stability theory combining with stochastic analysis approaches are employed to derive sufficient criteria ensuring the system to be globally exponentially robustly stable in the mean square. Finally, a numerical example is given to demonstrate the effectiveness and conservativeness of our theoretical results.

Keywords

Lyapunov methods; asymptotic stability; delays; neural nets; reaction-diffusion systems; robust control; stability criteria; stochastic processes; Lyapunov stability theory; global exponential robust stability; mean square; mixed time delays; stochastic analysis approach; stochastic reaction-diffusion static neural networks; sufficient criteria; Biological neural networks; Delay effects; Delays; Neurons; Robust stability; Stochastic processes; Stability; Static neural networks; Stochastic; reaction-diffusion;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2013 Ninth International Conference on

Conference_Location

Shenyang

Type

conf

DOI

10.1109/ICNC.2013.6817935

Filename

6817935