On synchronization of competitive memristor-based neural networks by nonlinear control
On synchronization of competitive memristor-based neural networks by nonlinear control
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DOI：
10.1016/j.neucom.2020.05.061
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发表时间：
2020-10
期刊：
影响因子：
6
通讯作者：
Chengde Zheng;Lulu Zhang
中科院分区：
文献类型：
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作者：
Chengde Zheng;Lulu Zhang
研究了延迟竞争忆阻神经网络的同步问题。首先，提出了一个新的松弛积分不等式，以减小基于辅助函数的积分不等式（AFBI）的估计误差。其次，利用勒让德多项式和适当的松散变量，延迟产品功能（DPF）的先进使用额外的自由度和更多的信息，系统的状态。在此基础上，提出了非线性控制器和Lyapunov-Krasovskii泛函（LKF），并利用Chen等人提出的基于时滞的全局同步原理，实现了系统的全局同步。的积分不等式和二次组合技巧。最后，通过算例验证了所得结果的有效性.
This paper probes into the synchronization of delayed competitive memristor-based neural networks (CMNNs). Firstly, a new loosened integral inequality is advanced to decrease the estimation difference of auxiliary function-based integral inequalities (AFBIs). Next, by utilizing Legendre polynomials and appropriate loose variables, a delay-product function (DPF) is advanced to employ extra freedom and more information on system states. Then, by proposing nonlinear controller and Lyapunov-Krasovskii functional (LKF) based on above DPF, two delay-dependent principles are offered to accomplish the global synchronization by making use of Chen et al.’s integral inequalities and quadratic combination technique. Finally, an example is supplied to reveal the effectiveness of the presented results.