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• W2029689911 abstract "This paper is concerned with the stability analysis problem for a new class of discrete-time recurrent neural networks with mixed time-delays. The mixed time-delays that consist of both the discrete and distributed time-delays are addressed, for the first time, when analyzing the asymptotic stability for discrete-time neural networks. The activation functions are not required to be differentiable or strictly monotonic. The existence of the equilibrium point is first proved under mild conditions. By constructing a new Lyapnuov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the discrete-time neural networks to be globally asymptotically stable. As an extension, we further consider the stability analysis problem for the same class of neural networks but with state-dependent stochastic disturbances. All the conditions obtained are expressed in terms of LMIs whose feasibility can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition." @default.
• W2029689911 created "2016-06-24" @default.
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• W2029689911 date "2009-01-01" @default.
• W2029689911 modified "2023-09-29" @default.
• W2029689911 title "Asymptotic stability for neural networks with mixed time-delays: The discrete-time case" @default.
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• W2029689911 doi "https://doi.org/10.1016/j.neunet.2008.10.001" @default.
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