Taking into accounting time-varying delays and anti-periodic environments, this paper deals with the global convergence dynamics on a class of anti-periodic high-order inertial Hopfield neural networks. First of all, with the help of Lyapunov function method, we prove that the global solutions are exponentially attractive to each other. Secondly, by using analytical techniques in uniform convergence functions sequence, the existence of the anti-periodic solution and its global exponential stability are established. Finally, two examples are arranged to illustrate the effectiveness and feasibility of the obtained results.

中文翻译：

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时变时滞的高阶惯性Hopfield神经网络的反周期动力学

考虑到时变时滞和反周期环境，本文研究了一类反周期高阶惯性Hopfield神经网络的全局收敛动力学。首先，借助Lyapunov函数方法，我们证明了全局解对彼此具有指数吸引力。其次，通过采用一致收敛函数序列的解析技术，建立了反周期解的存在性及其全局指数稳定性。最后，通过两个例子说明了所得结果的有效性和可行性。