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dc.contributor.authorWu, Z.-
dc.contributor.authorShi, P.-
dc.contributor.authorSu, H.-
dc.contributor.authorChu, J.-
dc.identifier.citationInternational Journal of Systems Science, 2012; 43(4):647-655-
dc.description.abstractThis article deals with the problem of delay-dependent state estimation for discrete-time neural networks with time-varying delay. Our objective is to design a state estimator for the neuron states through available output measurements such that the error state system is guaranteed to be globally exponentially stable. Based on the linear matrix inequality approach, a delay-dependent condition is developed for the existence of the desired state estimator via a novel Lyapunov functional. The obtained condition has less conservativeness than the existing ones, which is demonstrated by a numerical example.-
dc.description.statementofresponsibilityZhengguang Wu, Peng Shi, Hongye Su and Jian Chu-
dc.publisherTaylor & Francis Ltd-
dc.rights© 2012 Taylor & Francis-
dc.subjectneural networks-
dc.subjecttime-varying delay-
dc.subjectstate estimation-
dc.subjectexponential stability-
dc.subjectlinear matrix inequality (LMI)-
dc.titleState estimation for discrete-time neural networks with time-varying delay-
dc.typeJournal article-
dc.identifier.orcidShi, P. [0000-0001-8218-586X]-
Appears in Collections:Aurora harvest 4
Electrical and Electronic Engineering publications

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