Adaptive Synchronization of Reaction Diffusion Neural Networks With Infinite Distributed Delays and Stochastic Disturbance

In this paper, the diffusion effect, distributed delays and stochastic disturbance are involved in constructing the model of neural networks. Then, the global exponential synchronization problem is investigated for a class of reaction diffusion neural networks (RDNNs) with infinite distributed delays and stochastic disturbance. By employing the stochastic analysis method and Lyapunov functional theory, an adaptive controller is designed to guarantee the exponential synchronization of the drive and response RDNNs. The derived synchronization conditions are simple and the theoretical results can be directly extended to other RDNNs with or without distributed delays and stochastic disturbance. Finally, one example is provided to verify the effectiveness of the theoretical results and adaptive control approach.