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Discrete Dynamics in Nature and Society
Volume 2015, Article ID 278571, 12 pages
http://dx.doi.org/10.1155/2015/278571
Research Article

Robustness Analysis of Hybrid Stochastic Neural Networks with Neutral Terms and Time-Varying Delays

1College of Mathematics and Statistics, South Central University for Nationalities, Wuhan 430074, China
2Department of Mathematics and Computer Science, Liuzhou Teachers College, Liuzhou 546100, China
3College of Science, Huazhong Agriculture University, Wuhan 430070, China

Received 5 February 2015; Revised 2 May 2015; Accepted 2 May 2015

Academic Editor: Zidong Wang

Copyright © 2015 Chunmei Wu et al. 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

We analyze the robustness of global exponential stability of hybrid stochastic neural networks subject to neutral terms and time-varying delays simultaneously. Given globally exponentially stable hybrid stochastic neural networks, we characterize the upper bounds of contraction coefficients of neutral terms and time-varying delays by using the transcendental equation. Moreover, we prove theoretically that, for any globally exponentially stable hybrid stochastic neural networks, if additive neutral terms and time-varying delays are smaller than the upper bounds arrived, then the perturbed neural networks are guaranteed to also be globally exponentially stable. Finally, a numerical simulation example is given to illustrate the presented criteria.