Quasi-synchronization of Hybrid Coupled Reaction-diffusion Neural Networks with Parameter Mismatches via Time-space Sampled-data Control

2021 ◽  
Author(s):  
Xingru Li ◽  
Xiaona Song ◽  
Zhaoke Ning ◽  
Junwei Lu
Keyword(s):  
Neural Networks ◽  
Reaction Diffusion ◽  
Sampled Data ◽  
Data Control ◽  
Time Space ◽  
Sampled Data Control ◽  
Coupled Reaction

Synchronization for hybrid coupled reaction-diffusion neural networks with stochastic disturbances via spatial sampled-data control strategy

2020 ◽  
pp. 095965182093566
Author(s):  
Xiaona Song ◽  
Xingru Li ◽  
Zhaoke Ning ◽  
Mi Wang ◽  
Jingtao Man
Keyword(s):  
Neural Networks ◽  
Controller Design ◽  
Sufficient Conditions ◽  
Coupling Reaction ◽  
Reaction Diffusion ◽  
Sampled Data ◽  
Stochastic Disturbances ◽  
Digital Controllers ◽  
Data Control ◽  
Sampled Data Control

The synchronization of reaction-diffusion neural networks with state and spatial couplings is investigated in this article, and the time-varying delay and stochastic disturbances are considered in the proposed systems. Due to the development and merits of digital controllers, sampled-data control is a natural choice to establish synchronization in continuous-time systems. Here, we suggest a spatial sampled-data controller design, where the sampled-data (in space) measurements of the state are taken in a finite number of fixed sampling points in the spatial domain. It is assumed that the sampling intervals in space are bounded. Based on the Lyapunov stability theory, Young’s and Wirtinger’s inequalities techniques, some sufficient conditions are presented to synchronize the hybrid coupling reaction-diffusion neural networks with stochastic disturbances. Finally, the efficiency of the derived criteria will be demonstrated by resorting to two numerical examples.

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