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Type: Journal article
Title: Asynchronous filtering for Markov jump neural networks with quantized outputs
Author: Shen, Y.
Wu, Z.
Shi, P.
Su, H.
Huang, T.
Citation: IEEE Transactions on Systems Man and Cybernetics: Systems, 2018; 49(2):433-443
Publisher: IEEE
Issue Date: 2018
ISSN: 2168-2216
Statement of
Ying Shen, Zheng-Guang Wu, Peng Shi, Hongye Su, and Tingwen Huang
Abstract: In this paper, an asynchronous filter is proposed for Markov jump neural networks (NNs) with time delay and quantized measurements where a logarithmic quantizer is employed. The filter and quantizer are both mode-dependent and their modes are asynchronous with that of the NN, which is described by hidden Markov models. By the Lyapunov–Krasovskii functional approach, a sufficient condition is derived and a filter is then designed such that the filtering error dynamics are stochastically mean square stable and strictly (U ,S, V )-dissipative. Finally, the effectiveness and practicability of the theoretical results are verified by two examples, including a biological network.
Keywords: Asynchronous filter; asynchronous quantization; dissipativity; hidden Markov model; Markov jump neural networks (MJNNs)
Rights: © 2018 IEEE
RMID: 0030083065
DOI: 10.1109/TSMC.2017.2789180
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Appears in Collections:Electrical and Electronic Engineering publications

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