Security event-triggered control for markovian jump neural networks against actuator saturation and hybrid cyber attacks

2021 ◽  
Author(s):  
Yahan Deng ◽  
Hongqian Lu ◽  
Wuneng Zhou
Keyword(s):  
Neural Networks ◽  
Actuator Saturation ◽  
Cyber Attacks ◽  
Markovian Jump ◽  
Security Event ◽  
Event Triggered ◽  
Markovian Jump Neural Networks

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

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Jinxia Wang ◽  
Jinfeng Gao ◽  
Tian Tan ◽  
Jiaqi Wang ◽  
Miao Ma
Keyword(s):  
Neural Networks ◽  
Communication Network ◽  
Communication Networks ◽  
Filter Design ◽  
Sufficient Conditions ◽  
Cyber Attacks ◽  
Markovian Jump ◽  
Missing Measurements ◽  
Event Triggered ◽  
Markovian Jump Neural Networks

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.

Asynchronous finite-time state estimation for semi-Markovian jump neural networks with randomly occurred sensor nonlinearities

2021 ◽  
Vol 432 ◽  
pp. 240-249
Author(s):  
Yao Wang ◽  
Shengyuan Xu ◽  
Yongmin Li ◽  
Yuming Chu ◽  
Zhengqiang Zhang
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Finite Time ◽  
Markovian Jump ◽  
Markovian Jump Neural Networks
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