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Mean Square Exponential Stability of Uncertain Stochastic Hopfield Neural Networks with Interval Time-Varying Delays.

Authors :
Carbonell, Jaime G.
Siekmann, Jörg
De-Shuang Huang
Heutte, Laurent
Loog, Marco
Jiqing Qiu
Hongjiu Yang
Yuanqing Xia
Jinhui Zhang
Source :
Advanced Intelligent Computing Theories & Applications. With Aspects of Artificial Intelligence; 2007, p110-119, 10p
Publication Year :
2007

Abstract

The problem of mean square exponential stability of uncertain stochastic Hopfield neural networks with interval time-varying delays is investigated in this paper. The delay factor is assumed to be time-varying and belongs to a given interval, which means that the derivative of the delay function can exceed one. The uncertainties considered in this paper are norm-bounded and possibly time-varying. By Lyapunov-Krasovskii functional approach and stochastic analysis approach, a new delay-dependent stability criteria for the exponential stability of stochastic Hopfield neural networks is derived in terms of linear matrix inequalities(LMIs). A simulation example is given to demonstrate the effectiveness of the developed techniques. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540742012
Database :
Complementary Index
Journal :
Advanced Intelligent Computing Theories & Applications. With Aspects of Artificial Intelligence
Publication Type :
Book
Accession number :
33100556
Full Text :
https://doi.org/10.1007/978-3-540-74205-0_13