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

This paper investigates the quasi-synchronization of reaction-diffusion neural networks with hybrid coupling and parameter mismatches via sampled-data control technology. First, the models of neural networks with switching parameter and fraction Brownian motion are given. As a result of parameter mismatches, synchronization is normally not possible to realize directly, then the improved Halanay’s inequality is introduced, which is an important lemma to prove that the considered networks realize quasi-synchronization. Furthermore, based on stochastic theory, Lyapunov function method and inequality techniques, some sufficient conditions are derived to guarantee the quasi-synchronization of hybrid coupled neural networks with reaction-diffusion terms driven by fractional Brownian motion. Finally, two simulation examples are given to prove the efficiency of the developed criteria.