Event-Triggered H∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

Event-Triggered H∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

This paper concentrates on the event-triggered H∞ filter design for the discrete-time Markovian jump neural networks under random missing measurements and cyber attacks. Considering that the controlled system and the filtering can exchange information over a shared communication network which is vul...

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Main Authors: Jinxia Wang, Jinfeng Gao, Tian Tan, Jiaqi Wang, Miao Ma
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/4151542
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spelling doaj-f74496c24ee34c0daf7c15e9e75bfde92021-01-11T02:22:11ZengHindawi-WileyComplexity1099-05262020-01-01202010.1155/2020/4151542Event-Triggered H∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception AttacksJinxia Wang0Jinfeng Gao1Tian Tan2Jiaqi Wang3Miao Ma4Faculty of Mechanical Engineering and AutomationFaculty of Mechanical Engineering and AutomationFaculty of Mechanical Engineering and AutomationFaculty of Mechanical Engineering and AutomationFaculty of Mechanical Engineering and AutomationThis paper concentrates on the event-triggered H∞ filter design for the discrete-time Markovian jump neural networks under random missing measurements and cyber attacks. Considering that the controlled system and the filtering can exchange information over a shared communication network which is vulnerable to the cyber attacks and has limited bandwidth, the event-triggered mechanism is proposed to relieve the communication burden of data transmission. A variable conforming to Bernoulli distribution is exploited to describe the stochastic phenomenon since the missing measurements occur with random probability. Furthermore, seeing that the communication networks are vulnerable to external malicious attacks, the transferred information via the shared communication network may be changed by the injected false information from the attackers. Based on the above consideration, sufficient conditions for the filtering error system to maintain asymptotically stable are provided with predefined H∞ performance. In the end, three numerical examples are given to verify the proposed theoretical results.http://dx.doi.org/10.1155/2020/4151542
collection DOAJ
language English
format Article
sources DOAJ
author Jinxia Wang
Jinfeng Gao
Tian Tan
Jiaqi Wang
Miao Ma
spellingShingle Jinxia Wang
Jinfeng Gao
Tian Tan
Jiaqi Wang
Miao Ma
Event-Triggered H∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks
Complexity
author_facet Jinxia Wang
Jinfeng Gao
Tian Tan
Jiaqi Wang
Miao Ma
author_sort Jinxia Wang
title Event-Triggered H∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks
title_short Event-Triggered H∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks
title_full Event-Triggered H∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks
title_fullStr Event-Triggered H∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks
title_full_unstemmed Event-Triggered H∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks
title_sort event-triggered h∞ filtering for markovian jump neural networks under random missing measurements and deception attacks
publisher Hindawi-Wiley
series Complexity
issn 1099-0526
publishDate 2020-01-01
description This paper concentrates on the event-triggered H∞ filter design for the discrete-time Markovian jump neural networks under random missing measurements and cyber attacks. Considering that the controlled system and the filtering can exchange information over a shared communication network which is vulnerable to the cyber attacks and has limited bandwidth, the event-triggered mechanism is proposed to relieve the communication burden of data transmission. A variable conforming to Bernoulli distribution is exploited to describe the stochastic phenomenon since the missing measurements occur with random probability. Furthermore, seeing that the communication networks are vulnerable to external malicious attacks, the transferred information via the shared communication network may be changed by the injected false information from the attackers. Based on the above consideration, sufficient conditions for the filtering error system to maintain asymptotically stable are provided with predefined H∞ performance. In the end, three numerical examples are given to verify the proposed theoretical results.
url http://dx.doi.org/10.1155/2020/4151542
work_keys_str_mv AT jinxiawang eventtriggeredhfilteringformarkovianjumpneuralnetworksunderrandommissingmeasurementsanddeceptionattacks
AT jinfenggao eventtriggeredhfilteringformarkovianjumpneuralnetworksunderrandommissingmeasurementsanddeceptionattacks
AT tiantan eventtriggeredhfilteringformarkovianjumpneuralnetworksunderrandommissingmeasurementsanddeceptionattacks
AT jiaqiwang eventtriggeredhfilteringformarkovianjumpneuralnetworksunderrandommissingmeasurementsanddeceptionattacks
AT miaoma eventtriggeredhfilteringformarkovianjumpneuralnetworksunderrandommissingmeasurementsanddeceptionattacks
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