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New sufficient conditions on global asymptotic synchronization of inertial delayed neural networks by using integrating inequality techniques

机译:使用整合不等式技术对惯性延迟神经网络全局渐近同步的新的充分条件

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In this paper, the global asymptotic synchronization of a class of inertial delayed neural networks is investigated. Instead of using conventional study methods of global exponential/asymptotic synchronization: linear matrix inequality method, matrix measure strategy and stability theory methods, by using constructed integrating inequality and inequality techniques, we present two new sufficient conditions on global asymptotic synchronization for the drive-response inertial delayed neural networks under two new controllers by using different Lyapunov functions from those used in the existing papers. The presented results are more concise and easy to verify in practice than those obtained in existing papers. Hence, our results extend the study method of global synchronization for delayed neural networks.
机译:本文研究了一类惯性延迟神经网络的全局渐近同步。 而不是使用全局指数/渐近同步的传统研究方法:线性矩阵不等式方法,矩阵测量策略和稳定性理论方法,通过使用构造的整合不等式和不等式技术,我们为驱动响应的全局渐近同步提出了两个新的充分条件 通过使用不同的Lyapunov函数从现有论文中使用的偶然的Lyapunov函数下的惯性延迟神经网络。 在实践中比在现有论文中获得的结果更简洁且易于验证。 因此,我们的结果扩展了延迟神经网络的全局同步研究方法。

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