Non-fragile Extended dissipative synchronization of Markov jump Inertial Neural Networks: An event-triggered control strategy

2021 ◽  
Author(s):  
Tian Fang ◽  
Shiyu Jiao ◽  
Dongmei Fu ◽  
Jing Wang
Keyword(s):  
Neural Networks ◽  
Control Strategy ◽  
Markov Jump ◽  
Inertial Neural Networks ◽  
Event Triggered

Event-triggered Non-fragile State Estimation for Discrete Nonlinear Markov Jump Neural Networks with Sensor Failures

2019 ◽  
Vol 17 (5) ◽  
pp. 1131-1140 ◽  
Author(s):  
Jianning Li ◽  
Zhujian Li ◽  
Yufei Xu ◽  
Kaiyang Gu ◽  
Wendong Bao ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Markov Jump ◽  
Fragile State ◽  
Sensor Failures ◽  
Event Triggered

scholarly journals Stochastically exponential synchronization for Markov jump neural networks with time-varying delays via event-triggered control scheme

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiaoman Liu ◽  
Haiyang Zhang ◽  
Jun Yang ◽  
Hao Chen
Keyword(s):  
Neural Networks ◽  
Convex Combination ◽  
Analytical Techniques ◽  
Exponential Synchronization ◽  
Time Varying ◽  
Markov Jump ◽  
Synchronization Problem ◽  
Control Scheme ◽  
Reciprocally Convex Combination ◽  
Event Triggered

AbstractThis paper focuses on the stochastically exponential synchronization problem for one class of neural networks with time-varying delays (TDs) and Markov jump parameters (MJPs). To derive a tighter bound of reciprocally convex quadratic terms, we provide an improved reciprocally convex combination inequality (RCCI), which includes some existing ones as its particular cases. We construct an eligible stochastic Lyapunov–Krasovskii functional to capture more information about TDs, triggering signals, and MJPs. Based on a well-designed event-triggered control scheme, we derive several novel stability criteria for the underlying systems by employing the new RCCI and other analytical techniques. Finally, we present two numerical examples to show the validity of our methods.

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