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dc.contributor.authorShi, P.-
dc.contributor.authorLi, F.-
dc.contributor.authorWu, L.-
dc.contributor.authorLim, C.-
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2017; 28(9):2101-2114-
dc.description.abstractThis paper investigates the problem of exponential passive filtering for a class of stochastic neutral-type neural networks with both semi-Markovian jump parameters and mixed time delays. Our aim is to estimate the states by designing a Luenberger-type observer, such that the filter error dynamics are mean-square exponentially stable with an expected decay rate and an attenuation level. Sufficient conditions for the existence of passive filters are obtained, and a convex optimization algorithm for the filter design is given. In addition, a cone complementarity linearization procedure is employed to cast the nonconvex feasibility problem into a sequential minimization problem, which can be readily solved by the existing optimization techniques. Numerical examples are given to demonstrate the effectiveness of the proposed techniques.-
dc.description.statementofresponsibilityPeng Shi, Fanbiao Li, Ligang Wu and Cheng-Chew Lim-
dc.rights© 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.-
dc.subjectFiltering; neural networks (NNs); semi-Markovian jump systems (S-MJSs); time delay-
dc.titleNeural network-based passive filtering for delayed neutral-type semi-Markovian jump systems-
dc.typeJournal article-
dc.identifier.orcidShi, P. [0000-0001-8218-586X]-
dc.identifier.orcidLim, C. [0000-0002-2463-9760]-
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Electrical and Electronic Engineering publications

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