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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 486257, 10 pages
http://dx.doi.org/10.1155/2013/486257
Research Article

Exponential Stability Results of Discrete-Time Stochastic Neural Networks with Time-Varying Delays

Department of Electronics and Information Engineering, Shunde Polytechnic, Foshan 528300, China

Received 31 January 2013; Accepted 7 April 2013

Academic Editor: Weihai Zhang

Copyright © 2013 Yajun Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An innovative stability analysis approach for a class of discrete-time stochastic neural networks (DSNNs) with time-varying delays is developed. By constructing a novel piecewise Lyapunov-Krasovskii functional candidate, a new sum inequality is presented to deal with sum items without ignoring any useful items, the model transformation is no longer needed, and the free weighting matrices are added to reduce the conservatism in the derivation of our results, so the improvement of computational efficiency can be expected. Numerical examples and simulations are also given to show the effectiveness and less conservatism of the proposed criteria.