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Title: Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
Authors: Wang, Z
Liu, Y
Li, M
Liu, X
Keywords: Cohen–Grossberg neural networks; discrete delays; distributed delays; global asymptotic stability; linear matrix inequality (LMI);Lyapunov–Krasovskii functional; stochastic systems
Issue Date: 2006
Publisher: IEEE
Citation: IEEE Transactions on Neural Networks, 17(3): 814-820
Abstract: In this letter, the global asymptotic stability analysis problem is considered for a class of stochastic Cohen-Grossberg neural networks with mixed time delays, which consist of both the discrete and distributed time delays. Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, a linear matrix inequality (LMI) approach is developed to derive several sufficient conditions guaranteeing the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the addressed stochastic Cohen-Grossberg neural networks with mixed delays are globally asymptotically stable in the mean square if two LMIs are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox. It is also pointed out that the main results comprise some existing results as special cases. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria.
Description: Copyright [2006] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
ISSN: 1045-9227
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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