\(H_\infty\) state estimation for discrete-time neural networks with distributed delays and randomly occurring uncertainties through fading channels

DOI10.1016/j.neunet.2016.12.004zbMath1441.93300OpenAlexW2588416939WikidataQ47907496 ScholiaQ47907496MaRDI QIDQ2181043

Weijian Ren, Hongli Dong, Zidong Wang, Nan Hou, Fuad Eid S. Alsaadi

Publication date: 18 May 2020

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2016.12.004

zbMATH Keywords

state estimationrecurrent neural networksrandomly occurring uncertaintieschannel fadingsinfinitely distributed delays

Mathematics Subject Classification ID

Discrete-time control/observation systems (93C55) Estimation and detection in stochastic control theory (93E10) (H^infty)-control (93B36) Delay control/observation systems (93C43) Networked control (93B70)

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Cites Work

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• Global Attractivity in Delayed Hopfield Neural Network Models
• Linear Matrix Inequalities in System and Control Theory
• Distributed filtering with randomly occurring uncertainties over sensor networks: the channel fading case
• State estimation for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays

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