In this paper, we approximate a nonautonomous neural network with infinite delay and a Heaviside signal function by neural networks with sigmoidal signal functions. We show that the solutions of the sigmoidal models converge to those of the Heaviside inclusion as the sigmoidal parameter vanishes. In addition, we prove the existence of pullback attractors in both cases, and the convergence of the attractors of the sigmoidal models to those of the Heaviside inclusion.

中文翻译：

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具有无限时滞和重函数的非自治神经网络的S形逼近。

在本文中，我们通过具有S形信号函数的神经网络来逼近具有无限延迟和Heaviside信号函数的非自治神经网络。我们表明，随着S形参数消失，S形模型的解收敛到Heaviside包含的解。此外，我们证明了在两种情况下都存在回拉吸引子，并且乙状结肠模型的吸引子与重组分夹杂物的吸引子具有收敛性。