论文标题
具有乙状结肠功能的卷积神经网络连续近似
Continuous approximation by convolutional neural networks with a sigmoidal function
论文作者
论文摘要
在本文中，我们介绍了一类卷积神经网络（CNN）在CNN近似能力的研究中称为非重叠的CNN。我们证明，具有Sigmoidal激活函数的此类网络能够近似于紧凑型输入集定义的任意连续函数，并具有任何所需的准确性。此结果扩展了现有结果，只有多层馈电网络是一类近似值。评估阐明了我们结果的准确性和效率，并表明所提出的非重叠的CNN对噪声敏感。
In this paper we present a class of convolutional neural networks (CNNs) called non-overlapping CNNs in the study of approximation capabilities of CNNs. We prove that such networks with sigmoidal activation function are capable of approximating arbitrary continuous function defined on compact input sets with any desired degree of accuracy. This result extends existing results where only multilayer feedforward networks are a class of approximators. Evaluations elucidate the accuracy and efficiency of our result and indicate that the proposed non-overlapping CNNs are less sensitive to noise.
