Stochastic dissipativity analysis on discrete-time neural networks with time-varying delays

Authors:
Qiankun Song
Affiliations:
Department of Mathematics, Chongqing Jiaotong University, Chongqing 400074, China
Venue:
Neurocomputing
Year:
2011

Citing 12
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Abstract

In this paper, the problems of global dissipativity and global exponential dissipativity are investigated for discrete-time stochastic neural networks with time-varying delays and general activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and employing stochastic analysis technique, several new delay-dependent criteria for checking the global dissipativity and global exponential dissipativity of the addressed neural networks are established in linear matrix inequalities (LMIs). Furthermore, when the parameter uncertainties appear in the discrete-time stochastic neural networks with time-varying delays, the delay-dependent robust dissipativity criteria are also presented. Two examples are given to show the effectiveness and less conservatism of the proposed criteria.