Novel robust stability criteria for uncertain stochastic Hopfield neural networks with time-varying delays
Zhang, Jinhui and Shi, Peng and Qiu, Jiqing (2007) Novel robust stability criteria for uncertain stochastic Hopfield neural networks with time-varying delays. Nonlinear Analysis: Real World Applications, 8 (4). pp. 1349-1357. ISSN 1468-1218Full text for this resource is not available from the Research Repository.
The problem of stochastic robust stability of a class of stochastic Hopfield neural networks with time-varying delays and parameter uncertainties is investigated in this paper. The parameter uncertainties are time-varying and norm-bounded. The time-delay factors are unknown and time-varying with known bounds. Based on Lyapunov–Krasovskii functional and stochastic analysis approaches, some new stability criteria are presented in terms of linear matrix inequalities (LMIs) to guarantee the delayed neural network to be robustly stochastically asymptotically stable in the mean square for all admissible uncertainties. Numerical examples are given to illustrate the effectiveness and less conservativeness of the developed techniques.
|Uncontrolled Keywords:||ResPubID18801, stochastic stability, Hopfield neural networks, time-varying delays, robust stability, linear matrix inequalities(LMIs), norm-bounded uncertainty|
|Subjects:||Faculty/School/Research Centre/Department > Institute for Logistics and Supply Chain Management (ILSCM)
FOR Classification > 0199 Other Mathematical Sciences Information Systems
|Date Deposited:||27 Apr 2012 00:34|
|Last Modified:||27 Apr 2012 00:34|
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|Citations in Scopus:||101 - View on Scopus|
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