Global dissipativity of stochastic neural networks with time delay |
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| Authors: | Guanjun Wang Jinde Cao Lan Wang |
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| Institution: | Department of Mathematics, Southeast University, Nanjing 210096, China |
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| Abstract: | Liao and Wang Global dissipativity of continuous-time recurrent neural networks with time delay, Phys. Rev. E 68 (2003) 016118] firstly studied the dissipativity of neural networks. In this paper, the neural network model is generalized to a stochastic case, and the global dissipativity in mean of such stochastic system is investigated. By constructing several proper Lyapunov functionals combining with Jensen's inequality, Itô's formula and some analytic techniques, several sufficient conditions for the global dissipativity in mean of such stochastic neural networks are derived in LMIs forms, which can be easily verified in practice. Three numerical examples are provided to demonstrate the effectiveness of our criteria. |
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| Keywords: | Stochastic neural networks Global dissipativity in mean Attractive set in mean Lyapunov functional Itô 's formula Jensen's inequality Linear matrix inequality (LMI) |
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