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

This paper is concerned with the quasi-synchronization problem for a class of hybrid coupled neural networks with reaction diffusion, where the mismatched parameter and time-varying delay are considered in the system model. At first, a time-space sampled-data control is introduced, which not only effectively saves limited network bandwidth compared to traditional control strategies, but also improves the cyber-security of communications. Next, based on the Lyapunov function method and inequality techniques, some sufficient conditions are derived to guarantee the quasi-synchronization of hybrid coupled neural networks with mismatched parameter and reaction-diffusion, and the convergence region of quasi-synchronization is derived using the improved Halanay’s inequality. Finally, the validity and practicability of the derived criteria are verified by three numerical examples and an application example, respectively.