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Finite-Time and Fixed-Time Synchronization of Complex-Valued Recurrent Neural Networks with Discontinuous Activations and Time-Varying Delays

机译:具有不连续激活和时变延迟的复值经常性神经网络的有限时间和定时同步

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This paper is concerned with finite-time and fixed-time synchronization of complex-valued recurrent neural networks with discontinuous activations and time-varying delays. First, by separating the complex-valued recurrent neural networks into real and imaginary parts, we get subsystems with real values covered by the framework of differential inclusions, and novel time-delays feedback controllers are constructed to understand the synchronization problem in finite time and fixed time of error system. Second, by creating Lyapunov functions and applying some differential inequalities, several new criteria are derived to get the synchronization in finite time and fixed time of the studied neural networks. Finally, two numerical examples are presented to justify the effectiveness of our results.
机译:本文涉及具有不连续激活和时变延迟的复值经常性神经网络的有限时间和定时同步。首先,通过将复数的经常性神经网络分成真实和虚部,我们获得具有由差分夹杂物框架覆盖的实际值的子系统,并且构建了新的时延反馈控制器以了解有限时间和固定的同步问题错误系统的时间。其次,通过创建Lyapunov函数并应用一些差异不等式,导出了几种新标准,以在学习神经网络的有限时间和固定时间内获得同步。最后,提出了两个数值例子以证明我们结果的有效性。

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