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Type: Journal article
Title: Dissipativity-based resilient filtering of periodic markovian jump neural networks with quantized measurements
Author: Lu, R.
Tao, J.
Shi, P.
Su, H.
Wu, Z.
Xu, Y.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2018; 29(5):1888-1899
Publisher: IEEE
Issue Date: 2018
ISSN: 2162-237X
Statement of
Renquan Lu, Jie Tao, Peng Shi, Hongye Su, Zheng-Guang Wu, and Yong Xu
Abstract: The problem of dissipativity-based resilient filtering for discrete-time periodic Markov jump neural networks in the presence of quantized measurements is investigated in this paper. Due to the limited capacities of network medium, a logarithmic quantizer is applied to the underlying systems. Considering the fact that the filter is realized through a network, randomly occurring parameter uncertainties of the filter are modeled by two mode-dependent Bernoulli processes. By establishing the mode-dependent periodic Lyapunov function, sufficient conditions are given to ensure the stability and dissipativity of the filtering error system. The filter parameters are derived via solving a set of linear matrix inequalities. The merits and validity of the proposed design techniques are verified by a simulation example.
Keywords: Dissipativity
Neural networks
Periodic Markov jump systems
Resilient filter
Rights: © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
DOI: 10.1109/TNNLS.2017.2688582
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Electrical and Electronic Engineering publications

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