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Abstract and Applied Analysis
Volume 2014, Article ID 857341, 19 pages
http://dx.doi.org/10.1155/2014/857341
Research Article

Exponential Stability of Periodic Solutions for Inertial Type BAM Cohen-Grossberg Neural Networks

Department of Mathematics, Shaoxing University, Shaoxing, Zhejiang 312000, China

Received 6 February 2014; Revised 22 March 2014; Accepted 3 April 2014; Published 19 May 2014

Academic Editor: Zidong Wang

Copyright © 2014 Chunfang Miao and Yunquan Ke. 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

The existence and exponential stability of periodic solutions for inertial type BAM Cohen-Grossberg neural networks are investigated. First, by properly choosing variable substitution, the system is transformed to first order differential equation. Second, some sufficient conditions that ensure the existence and exponential stability of periodic solutions for the system are obtained by constructing suitable Lyapunov functional and using differential mean value theorem and inequality technique. Finally, two examples are given to illustrate the effectiveness of the results.