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dc.contributor.authorLiu, Y-
dc.contributor.authorWang, Z-
dc.contributor.authorLiu, X-
dc.identifier.citationNeural Networks, 19(5): 667-675, Jun 2006en_US
dc.descriptionThis is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.en_US
dc.description.abstractThis paper is concerned with analysis problem for the global exponential stability of a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, by employing a new Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally exponentially stable. Therefore, the global exponential stability of the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.en_US
dc.description.sponsorshipThis work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany.en_US
dc.subjectGeneralized recurrent neural networksen_US
dc.subjectDiscrete and distributed delaysen_US
dc.subjectLyapunov–Krasovskii functionalen_US
dc.subjectGlobal exponential stabilityen_US
dc.subjectGlobal asymptotic stabilityen_US
dc.subjectLinear matrix inequalityen_US
dc.titleGlobal exponential stability of generalized recurrent neural networks with discrete and distributed delaysen_US
dc.typeResearch Paperen_US
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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