Matrix measure based exponential stabilization for complex-valued inertial neural networks with time-varying delays using impulsive control

2018 ◽  
Vol 273 ◽  
pp. 251-259 ◽  
Author(s):  
Qian Tang ◽  
Jigui Jian
Keyword(s):  
Neural Networks ◽  
Impulsive Control ◽  
Exponential Stabilization ◽  
Time Varying ◽  
Matrix Measure ◽  
Inertial Neural Networks ◽  
Complex Valued

scholarly journals Stochastic Exponential Stabilization for Markov Jump Neural Networks with Time-varying Delays via Adaptive Event-Triggered Impulsive Control

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiaoman Liu ◽  
Haiyang Zhang ◽  
Tao Wu ◽  
Jinlong Shu
Keyword(s):  
Neural Networks ◽  
Impulsive Control ◽  
Exponential Stabilization ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Control Cost ◽  
Markov Jump ◽  
Type Integral ◽  
Event Triggered

This paper focuses on the exponential stabilization problem for Markov jump neural networks with Time-varying Delays (TDs). Firstly, we provide a new Free-matrix-based Exponential-type Integral Inequality (FMEII) containing the information of attenuation exponent, which is helpful to reduce the conservativeness of stability criteria. To further save control cost, we introduce a sample-based Adaptive Event-triggered Impulsive Control (AEIC) scheme, in which the trigger threshold is adaptively varied with the sampled state. By fully considering the information about sampled state, TDs, and Markov jump parameters, a suitable Lyapunov–Krasovskii functional is constructed. With the virtue of FMEII and AEIC scheme, some novel stabilization criteria are presented in the form of linear matrix inequalities. At last, two numerical examples are given to show the validity of the obtained results.

scholarly journals Finite-time projective synchronization of stochastic complex-valued neural networks with probabilistic time-varying delays

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Meng Hui ◽  
Jiahuang Zhang ◽  
Jiao Zhang ◽  
Herbert Ho-Ching Iu ◽  
Rui Yao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Projective Synchronization ◽  
Time Varying ◽  
Complex Valued
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