Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/124274
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Type: Journal article
Title: Set-membership estimation for complex networks subject to linear and nonlinear bounded attacks
Author: Song, H.
Shi, P.
Lim, C.C.
Zhang, W.
Yu, L.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2020; 31(1):163-173
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2020
ISSN: 2162-237X
2162-2388
Statement of
Responsibility: 
Haiyu Song, Peng Shi, Cheng-Chew Lim, Wen-An Zhang, Li Yu
Abstract: This paper is concerned with the set-membership estimation problem for complex networks subject to unknown-but-bounded attacks. Adversaries are assumed to exist in the nonsecure communication channels from the nodes to the estimators. The transmitted measurements may be modified by an attack function with added noise that is determined by the adversary but unknown to the estimators. A novel set-membership estimation model against unknown-but-bounded attacks is presented. Two sufficient conditions are derived to guarantee the existence of the set-membership estimators for the cases that the attack functions are linear and nonlinear, respectively. Two strategies for the design of the set-membership estimator gains are presented. The effectiveness of the proposed estimator design method is verified by two simulation examples.
Keywords: Complex networks;, network attack; networked estimation; set-membership estimation
Rights: © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
DOI: 10.1109/TNNLS.2019.2900045
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Published version: http://dx.doi.org/10.1109/tnnls.2019.2900045
Appears in Collections:Aurora harvest 4
Electrical and Electronic Engineering publications

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