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Sigmoidal Function Classes for Feedforward Artificial Neural Networks

机译：前馈人工神经网络的S型函数类

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摘要

The role of activation functions in feedforward artificial neural networks has not been investigated to the desired extent. The commonly used sigmoidal functions appear as discrete points in the sigmoidal functional space. This makes comparison difficult. Moreover, these functions can be interpreted as the (suitably scaled) integral of some probability density function (generally taken to be symmetric/bell shaped). Two parameterization methods are proposed that allow us to construct classes of sigmoidal functions based on any given sigmoidal function. The suitability of the members of the proposed class is investigated. It is demonstrated that all members of the proposed class(es) satisfy the requirements to act as an activation function in feedforward artificial neural networks.
机译：尚未研究激活功能在前馈人工神经网络中的作用。常用的S型函数在S型函数空间中显示为离散点。这使得比较困难。此外，这些函数可以解释为某个概率密度函数（通常视为对称/钟形）的（适当缩放）的积分。提出了两种参数化方法，这些方法使我们可以基于任何给定的S型函数构造S型函数。研究了提议的班级成员的适合性。证明所提出的类的所有成员都满足在前馈人工神经网络中充当激活函数的要求。

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