Quasi-synchronization of coupled neural networks with reaction-diffusion terms driven by fractional brownian motion

2021 ◽  
Author(s):  
Xiaona Song ◽  
Xingru Li ◽  
Shuai Song ◽  
Yijun Zhang ◽  
Zhaoke Ning
Keyword(s):  
Neural Networks ◽  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Reaction Diffusion ◽  
Coupled Neural Networks

Dynamical behaviors of coupled neural networks with reaction-diffusion terms: analysis, control and applications

2017 ◽  
Vol 227 ◽  
pp. 1-2 ◽  
Author(s):  
Jin-Liang Wang ◽  
Tingwen Huang ◽  
Jinling Liang ◽  
Yang Tang ◽  
Jun Hu
Keyword(s):  
Neural Networks ◽  
Reaction Diffusion ◽  
Coupled Neural Networks ◽  
Dynamical Behaviors

Recent Advances on Dynamical Behaviors of Coupled Neural Networks With and Without Reaction–Diffusion Terms

2020 ◽  
Vol 31 (12) ◽  
pp. 5231-5244 ◽  
Author(s):  
Jin-Liang Wang ◽  
Shui-Han Qiu ◽  
Wei-Zhong Chen ◽  
Huai-Ning Wu ◽  
Tingwen Huang
Keyword(s):  
Neural Networks ◽  
Reaction Diffusion ◽  
Coupled Neural Networks ◽  
Dynamical Behaviors ◽  
Recent Advances

Extended dissipative-based state estimation for Markov jump coupled neural networks with reaction-diffusion terms

2021 ◽  
pp. 014233122110282
Author(s):  
Dongxiao Hu ◽  
Xiaona Song ◽  
Xingru Li
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Dynamic Error ◽  
Signal Transmission ◽  
Reaction Diffusion ◽  
Linear Matrix ◽  
Markov Jump ◽  
Memory Controller ◽  
Coupled Neural Networks ◽  
Error System

This work mainly concentrates on the state estimation for Markov jump coupled neural networks (MJCNNs) with reaction-diffusion terms, in which the memory controller is employed. First, the considered MJCNNs model is introduced, and the dynamic error system can be obtained based on the proposed state estimator. Then, a memory controller that involves constant signal transmission delay is designed. Moreover, by Lyapunov functional method, inequality technique and Kronecker product law, a novel stable and extended dissipative analysis criteria can be established to ensure that the stability of the error system the error system. Meanwhile, the controller gains can be obtained by solving linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the developed method.

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