Event-triggered stochastic synchronization in finite time for delayed semi-Markovian jump neural networks with discontinuous activations

2020 ◽  
Vol 39 (2) ◽  
Author(s):  
Min Liu ◽  
Huaiqin Wu ◽  
Wei Zhao
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Markovian Jump ◽  
Stochastic Synchronization ◽  
Discontinuous Activations ◽  
Event Triggered ◽  
Markovian Jump Neural Networks

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

scholarly journals Finite-Time H ∞ State Estimation for Markovian Jump Neural Networks with Time-Varying Delays via an Extended Wirtinger’s Integral Inequality

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Saravanan Shanmugam ◽  
M. Syed Ali ◽  
R. Vadivel ◽  
Gyu M. Lee
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Transition Probabilities ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Time Varying ◽  
Markovian Jump ◽  
Markovian Jumping ◽  
Double Inequality ◽  
Markovian Jump Neural Networks

This study investigates the finite-time boundedness for Markovian jump neural networks (MJNNs) with time-varying delays. An MJNN consists of a limited number of jumping modes wherein it can jump starting with one mode then onto the next by following a Markovian process with known transition probabilities. By constructing new Lyapunov–Krasovskii functional (LKF) candidates, extended Wirtinger’s, and Wirtinger’s double inequality with multiple integral terms and using activation function conditions, several sufficient conditions for Markovian jumping neural networks are derived. Furthermore, delay-dependent adequate conditions on guaranteeing the closed-loop system which are stochastically finite-time bounded (SFTB) with the prescribed H ∞ performance level are proposed. Linear matrix inequalities are utilized to obtain analysis results. The purpose is to obtain less conservative conditions on finite-time H ∞ performance for Markovian jump neural networks with time-varying delay. Eventually, simulation examples are provided to illustrate the validity of the addressed method.

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.

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