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Lag Synchronization of Memristor-Based Coupled Neural Networks via -Measure

机译：基于忆阻器的忆阻器耦合神经网络的滞后同步

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摘要

This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the -measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results.
机译：本文使用两种不同的算法处理了具有或不具有参数失配的基于忆阻器的耦合神经网络的滞后同步问题。首先，考虑具有参数失配的基于忆阻器的神经网络，由于参数失配而无法实现滞后完全同步，引入了滞后准同步的概念。基于测度方法和广义Halanay不等式，估计误差水平，提出了一种新的滞后准同步方案，以确保基于忆阻器的耦合神经网络处于滞后同步且误差水平。其次，通过构造Lyapunov泛函并应用常见的Halanary不等式，给出了带有参数匹配的基于忆阻器的神经网络的几个完全滞后同步准则，易于验证。最后，给出两个例子来说明所提出的滞后准同步或滞后完全同步准则的有效性，这很好地支持了理论结果。

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