Global dissipativity of uncertain discrete-time stochastic neural networks with time-varying delays

Authors:
Mengzhuo Luo;Shouming Zhong
Affiliations:
School of Science Mathematics, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China;School of Science Mathematics, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China and Key Laboratory for Neuroinformation of Ministry of Education, University ...
Venue:
Neurocomputing
Year:
2012

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ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I

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Abstract

In this letter, the problem on global exponential dissipativity in mean is investigated for uncertain discrete-time stochastic neural networks with time-varying delays. In the concerned model, the stochastic disturbance is described by the Brownian motion, and the time-varying delays in a given range. By constructing appropriate Lyapunov-Krasovskii functional (LKF), combining with Jensen's inequality, stochastic analysis method and the free-weighting matrix method, a new delay-dependent global dissipativity criteria is obtained in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in Matlab. Finally, three examples are exploited to show the usefulness of the results derived.