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Type: Journal article
Title: Resilient asynchronous H∞ filtering for Markov jump neural networks with unideal measurements and multiplicative noises
Other Titles: Resilient asynchronous H-infinity filtering for Markov jump neural networks with unideal measurements and multiplicative noises
Author: Zhang, L.
Zhu, Y.
Shi, P.
Zhao, Y.
Citation: IEEE Transactions on Cybernetics, 2015; 45(12):2840-2852
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2015
ISSN: 2168-2267
Statement of
Lixian Zhang, Yanzheng Zhu, Peng Shi and Yuxin Zhao
Abstract: This paper is concerned with the resilient H∞ filtering problem for a class of discrete-time Markov jump neural networks (NNs) with time-varying delays, unideal measurements, and multiplicative noises. The transitions of NNs modes and desired mode-dependent filters are considered to be asynchronous, and a nonhomogeneous mode transition matrix of filters is used to model the asynchronous jumps to different degrees that are also mode-dependent. The unknown time-varying delays are also supposed to be mode-dependent with lower and upper bounds known a priori. The unideal measurements model includes the phenomena of randomly occurring quantization and missing measurements in a unified form. The desired resilient filters are designed such that the filtering error system is stochastically stable with a guaranteed H∞ performance index. A monotonicity is disclosed in filtering performance index as the degree of asynchronous jumps changes. A numerical example is provided to demonstrate the potential and validity of the theoretical results.
Keywords: time-varying delays; Asynchronous jumps; missing measurements; multiplicative noises; quantization; resilient filter
Rights: © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030040559
DOI: 10.1109/TCYB.2014.2387203
Grant ID:
Appears in Collections:Electrical and Electronic Engineering publications

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