Discrete Dynamics in Nature and Society
Volume 2009 (2009), Article ID 490515, 20 pages
doi:10.1155/2009/490515
Research Article

Stochastic Stability of Neural Networks with Both Markovian Jump Parameters and Continuously Distributed Delays

Quanxin Zhu1,2 and Jinde Cao1

1Department of Mathematics, Southeast University, Nanjing 210096, Jiangsu, China
2Department of Mathematics, Ningbo University, Ningbo 315211, Zhejiang, China

Received 4 March 2009; Accepted 29 June 2009

Academic Editor: Manuel De La Sen

Copyright © 2009 Quanxin Zhu and Jinde Cao. 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 problem of stochastic stability is investigated for a class of neural networks with both Markovian jump parameters and continuously distributed delays. The jumping parameters are modeled as a continuous-time, finite-state Markov chain. By constructing appropriate Lyapunov-Krasovskii functionals, some novel stability conditions are obtained in terms of linear matrix inequalities (LMIs). The proposed LMI-based criteria are computationally efficient as they can be easily checked by using recently developed algorithms in solving LMIs. A numerical example is provided to show the effectiveness of the theoretical results and demonstrate the LMI criteria existed in the earlier literature fail. The results obtained in this paper improve and generalize those given in the previous literature.