Journal
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
Volume 349, Issue 6, Pages 1972-1988Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jfranklin.2012.04.005
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Funding
- National Natural Science Foundations of China [60973048, 60974025, 60673101, 60939003]
- National 863 Plan Project [2008AA04Z401, 2009AA043404]
- Natural Science Foundation of Shandong Province [Y2007G30]
- Natural Science Foundation of Guangxi Autonomous Region [2012GXNSFBA053003]
- Scientific and Technological Project of Shandong Province [2007GG3WZ04016]
- Science Foundation of Harbin Institute of Technology (Weihai) [HIT(WH)200807]
- Natural Scientific Research Innovation Foundation in Harbin Institute of Technology [HIT.NSRIF.2001120]
- China Postdoctoral Science Foundation [20100481000]
- Shandong Provincial Key Laboratory of Industrial Control Technique (Qingdao University)
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This paper is devoted to investigating the robust stochastic exponential stability for reaction-diffusion Cohen-Grossberg neural networks (RDCGNNs) with Markovian jumping parameters and mixed delays. The parameter uncertainties are assumed to be norm bounded. The delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Some criteria for delay-dependent robust exponential stability of RDCGNNs with Markovian jumping parameters are established in terms of linear matrix inequalities (LMIs), which can be easily checked by utilizing Matlab LMI toolbox. Numerical examples are provided to demonstrate the efficiency of the proposed results. (C) 2012 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
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