Synchronization of reaction-diffusion neural networks with sampled-data control via a new two-sided looped-functional

In this paper, the exponential synchronization problem of reaction-diffusion neural networks with sampled -data control is addressed via looped-functional approach. Considering the transmission delay of sampled-data controller, a two-sided looped-functional is designed to obtain the synchronization conditions, which are less conservative than those with the traditional Lyapunov method. The research results are applied to Takagi- Sugeno (T-S) fuzzy models with reaction-diffusion terms. Three numerical examples are presented to show the feasibility and effectiveness of our methods.

Automated summary

In this paper, the problem of exponential synchronization of reaction-diffusion neural networks with sampled-data control is addressed using a looped-functional approach. By considering the transmission delay of the sampled-data controller, a two-sided looped-functional is designed to obtain less conservative synchronization conditions than those obtained with the traditional Lyapunov method. The research results are applied to Takagi-Sugeno (T-S) fuzzy models with reaction-diffusion terms, and numerical examples are presented to demonstrate the feasibility and effectiveness of the proposed methods.