Resilient $$H_{\infty }$$ H ∞ Filtering for Stochastic Systems with Randomly Occurring Gain Variations, Nonlinearities and Channel Fadings-Reference-Cited by-同舟云学术

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Resilient $$H_{\infty }$$ H ∞ Filtering for Stochastic Systems with Randomly Occurring Gain Variations, Nonlinearities and Channel Fadings

Published:2019-03-13 Issue:10 Volume:38 Page:4548-4571
ISSN:0278-081X
Container-title:Circuits, Systems, and Signal Processing
language:en
Short-container-title:Circuits Syst Signal Process

Author:

Jiang Xiangli,Xia Guihua,Feng Zhiguang

Funder

National Natural Science Foundation of China and China Academy of Engineering Physics

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

China Postdoctoral Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Signal Processing

Link

http://link.springer.com/content/pdf/10.1007/s00034-019-01087-0.pdf

Reference39 articles.

1. F. Boem, Y. Zhou, C. Fischione, T. Parisini, Distributed Pareto-optimal state estimation using sensor networks. Automatica 93, 211–223 (2018)

2. S. Boyd, L. ElGhaoui, E. Feron, V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory (SIAM, Philadelphia, 1994)

3. X.H. Chang, J. Xiong, J. Park, Resilient $${H}_\infty $$ H ∞ filtering for discrete-time systems. Signal Process. 127(C), 71–79 (2016)

4. B. Chen, L. Yu, W. Zhang, $${H}_\infty $$ H ∞ filtering for Markovian switching genetic regulatory networks with time-delays and stochastic disturbances. Circuits Syst. Signal Process. 30(6), 1231–1252 (2011)

5. D. Ding, X. Li, Y. Wang, Z. Shi, Non-fragile fuzzy filtering for discrete-time non-linear systems. IET Control Theory Appl. 7(6), 848–857 (2013)

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