Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/109077
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Type: Journal article
Title: Neural network-based passive filtering for delayed neutral-type semi-Markovian jump systems
Author: Shi, P.
Li, F.
Wu, L.
Lim, C.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2017; 28(9):2101-2114
Publisher: IEEE
Issue Date: 2017
ISSN: 2162-237X
2162-2388
Statement of
Responsibility: 
Peng Shi, Fanbiao Li, Ligang Wu and Cheng-Chew Lim
Abstract: This 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.
Keywords: Filtering; neural networks (NNs); semi-Markovian jump systems (S-MJSs); time delay
Rights: © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
RMID: 0030050018
DOI: 10.1109/TNNLS.2016.2573853
Grant ID: http://purl.org/au-research/grants/arc/DP140102180
http://purl.org/au-research/grants/arc/LP140100471
Appears in Collections:Electrical and Electronic Engineering publications

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