Spacial sampled-data control for H∞ output synchronization of directed coupled reaction–diffusion neural networks with mixed delays

2020 ◽  
Vol 123 ◽  
pp. 429-440 ◽  
Author(s):  
Binglong Lu ◽  
Haijun Jiang ◽  
Cheng Hu ◽  
Abdujelil Abdurahman
Keyword(s):  
Neural Networks ◽  
Reaction Diffusion ◽  
Mixed Delays ◽  
Sampled Data ◽  
Output Synchronization ◽  
Data Control ◽  
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.

scholarly journals Synchronization of a Class of Memristive Stochastic Bidirectional Associative Memory Neural Networks with Mixed Time-Varying Delays via Sampled-Data Control

2018 ◽  
Vol 2018 ◽  
pp. 1-24 ◽  
Author(s):  
Manman Yuan ◽  
Weiping Wang ◽  
Xiong Luo ◽  
Chao Ge ◽  
Lixiang Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Stochastic Perturbation ◽  
Mixed Delays ◽  
Time Varying ◽  
Sampled Data ◽  
Bidirectional Associative Memory ◽  
Data Control ◽  
Sampled Data Control ◽  
Data Controller

The paper addresses the issue of synchronization of memristive bidirectional associative memory neural networks (MBAMNNs) with mixed time-varying delays and stochastic perturbation via a sampled-data controller. First, we propose a new model of MBAMNNs with mixed time-varying delays. In the proposed approach, the mixed delays include time-varying distributed delays and discrete delays. Second, we design a new method of sampled-data control for the stochastic MBAMNNs. Traditional control methods lack the capability of reflecting variable synaptic weights. In this paper, the methods are carefully designed to confirm the synchronization processes are suitable for the feather of the memristor. Third, sufficient criteria guaranteeing the synchronization of the systems are derived based on the derive-response concept. Finally, the effectiveness of the proposed mechanism is validated with numerical experiments.

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