Quasi-Synchronization of Delayed Chaotic Memristive Neural Networks
研究了两个延迟忆阻神经网络的主从同步问题，将忆阻器视为不确定时变参数，通过李雅普诺夫函数和不等式技术推导出准同步条件，并证明当钉扎强度超过阈值时可以实现预定误差范围内的同步。
We study the problem of master-slave synchronization of two delayed memristive neural networks (MNNs). Different from most previous papers, memristors are regarded as uncertain continuous time-varying parameters, and MNNs are modeled by neural networks (NNs) with continuous time-varying parameters and polytopic uncertainty. Thus, synchronization of two delayed MNNs is converted into synchronization of delayed NNs with uncertain parameter mismatches. Quasi-synchronization criteria are derived by Lyapunov function and inequality technique. It is shown that, given a predetermined error bound, quasi-synchronization of two delayed chaotic MNNs can be achieved provided that the pinning strength is larger than a threshold. In the end, a numerical example is provided to illustrate the effectiveness of the derived results.