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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 212469, 11 pages
Research Article

Stability Analysis of Stochastic Markovian Jump Neural Networks with Different Time Scales and Randomly Occurred Nonlinearities Based on Delay-Partitioning Projection Approach

1School of Science, Jiangnan University, Wuxi 214122, China
2Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education, Wuxi 214122, China

Received 27 June 2013; Accepted 2 October 2013

Academic Editor: Debora Amadori

Copyright © 2013 Jianmin Duan 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.


In this paper, the mean square asymptotic stability of stochastic Markovian jump neural networks with different time scales and randomly occurred nonlinearities is investigated. In terms of linear matrix inequality (LMI) approach and delay-partitioning projection technique, delay-dependent stability criteria are derived for the considered neural networks for cases with or without the information of the delay rates via new Lyapunov-Krasovskii functionals. We also obtain that the thinner the delay is partitioned, the more obviously the conservatism can be reduced. An example with simulation results is given to show the effectiveness of the proposed approach.