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Mathematical Problems in Engineering
Volume 2015, Article ID 140857, 8 pages
http://dx.doi.org/10.1155/2015/140857
Research Article

Input-to-State Stability of Stochastic Memristive Neural Networks with Time-Varying Delay

Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China

Received 24 September 2014; Accepted 10 January 2015

Academic Editor: Nazim I. Mahmudov

Copyright © 2015 Xu Y. Lou and Qian Ye. 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

This paper is concerned with the input-to-state stability problem of a class of memristive neural networks. We consider the neural networks that take into account both the stochastic effects and time-varying delay, and introduce the notions of meansquare exponential input-to-state stability. Using the stochastic analysis theory and Itô formula for stochastic differential equations, we establish sufficient conditions for both mean-square exponential input-to-state stability and mean-square exponential stability. Numerical simulations are also provided to demonstrate the theoretical results.