Stability of stochastically perturbed Hopfield-type neural networks with mixed delays

被引：0

作者：

Jendoubi, Chiraz ^{[1
]}

Cherif, Farouk ^{[2
]}

机构：

[1] Univ Sfax, Sfax Fac Sci, Sfax, Tunisia

[2] Univ Sousse, ISSATS, Sousse, Tunisia

来源：

2014 6TH INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR) | 2014年

关键词：

Neural networks; quadratic mean almost periodic; global stability; Ito's isometry; mixed delays; TIME-VARYING DELAYS; EXPONENTIAL STABILITY; NONLINEAR-SYSTEMS;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper gives some new and slacker conditions about stability of the square mean pseudo almost periodic solutions of the stochastic Hopfield-type neural networks with mixed delays dx (t) = [- Dx(t) + Af(x(t)) + Bg (x(tau)(t)) + integral(t)(t-rho) K(t - s) h (x (s)) ds] dt + sigma(x (t)) d omega (t) x (t) = xi(t), -tau <= t <= 0.

引用

页码：465 / 470

页数：6

共 21 条

[1]

Amir B., 1999, Ann. Math. Blaise Pascal, V6, P1

[2]

Ammar B., 2012, IEEE T NEURAL NETWOR, V23

[3] Authors' reply to "Comments on 'Finite-time stability theorem of stochastic nonlinear systems [Automatica 46 (2010) 2105-2108]'" [J].