Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118297
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Type: Journal article
Title: Nonfragile state estimation of quantized complex networks with switching topologies
Author: Wu, Z.
Xu, Z.
Shi, P.
Chen, M.
Su, H.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2018; 29(10):5111-5121
Publisher: IEEE
Issue Date: 2018
ISSN: 2162-237X
2162-2388
Statement of
Responsibility: 
Zheng-Guang Wu, Zhaowen Xu, Peng Shi, Michael Z.Q. Chen and Hongye Su
Abstract: This paper considers the nonfragile H∞ estimation problem for a class of complex networks with switching topologies and quantization effects. The network architecture is assumed to be dynamic and evolves with time according to a random process subject to a sojourn probability. The coupled signal is to be quantized before transmission due to power and bandwidth constraints, and the quantization errors are transformed into sector-bounded uncertainties. The concept of nonfragility is introduced by inserting randomly occurred uncertainties into the estimator parameters to cope with the unavoidable small gain variations emerging from the implementations of estimators. Both the quantizers and the estimators have several operation modes depending on the switching signal of the underlying network structure. A sufficient condition is provided via a linear matrix inequality approach to ensure the estimation error dynamic to be stochastically stable in the absence of external disturbances, and the H∞ performance with a prescribed index is also satisfied. Finally, a numerical example is presented to clarify the validity of the proposed method.
Keywords: Complex networks; nonfragile estimation; quantization effects; switching topologies
Rights: © 2018 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: 0030082759
DOI: 10.1109/TNNLS.2018.2790982
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Appears in Collections:Electrical and Electronic Engineering publications

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