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.

本文通过采样数据控制技术研究了具有混合耦合和参数不匹配的反应扩散神经网络的准同步。首先，给出了具有切换参数和分数布朗运动的神经网络模型。由于参数不匹配，通常无法直接实现同步，因此引入了改进的Halanay不等式，这是证明所考虑的网络实现准同步的重要引理。此外，基于随机理论，李雅普诺夫函数法和不等式技术，推导了一些充分的条件，以保证由分数布朗运动驱动的具有反应扩散项的混合耦合神经网络的准同步。最后，