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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Exponential Synchronization of Memristive Neural Networks With Delays: Interval Matrix Method
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Exponential Synchronization of Memristive Neural Networks With Delays: Interval Matrix Method

机译：时滞忆阻神经网络的指数同步：区间矩阵法

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摘要

This paper considers the global exponential synchronization of drive-response memristive neural networks (MNNs) with heterogeneous time-varying delays. Because the parameters of MNNs are state-dependent, the MNNs may exhibit unexpected parameter mismatch when different initial conditions are chosen. Therefore, traditional robust control scheme cannot guarantee the synchronization of MNNs. Under the framework of Filippov solution, the drive and response MNNs are first transformed into systems with interval parameters. Then suitable controllers are designed to overcome the problem of mismatched parameters and synchronize the coupled MNNs. Based on some novel Lyapunov functionals and interval matrix inequalities, several sufficient conditions are derived to guarantee the exponential synchronization. Moreover, adaptive control is also investigated for the exponential synchronization. Numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.
机译：本文考虑了具有时变异构时滞的驱动响应忆阻神经网络（MNN）的全局指数同步。由于MNN的参数取决于状态，因此当选择不同的初始条件时，MNN可能会出现意外的参数不匹配。因此，传统的鲁棒控制方案不能保证MNN的同步。在Filippov解决方案的框架下，首先将驱动和响应MNN转换为具有间隔参数的系统。然后设计合适的控制器来克服参数不匹配的问题，并使耦合的MNN同步。基于一些新颖的Lyapunov泛函和区间矩阵不等式，推导了几个足以保证指数同步的条件。此外，还针对指数同步研究了自适应控制。提供数值模拟以说明理论分析的有效性。

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