Abstract
This paper mainly concerns stochastically asymptotical stability analysis problems for a class of stochastic Cohen-Grossberg neural networks with mixed time delays and Markovian parameters SDCGNNswM). Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, a linear matrix inequality (LMI) approach is developed to derive the sufficient conditions guaranteeing the stochastically asymptotical stability of the equilibrium point. All the obtained results are presented in term of linear matrix inequalities. The efficiency of the proposed results is demonstrated via a numerical example.
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Lu, J., Wang, S., Zhang, C. (2011). Stability of Stochastic Cohen-Grossberg Neural Networks with Mixed Time Delay and Markovian Parameters. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21105-8_23
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DOI: https://doi.org/10.1007/978-3-642-21105-8_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21104-1
Online ISBN: 978-3-642-21105-8
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