论文标题
一种分析方法，用于计算馈送前馈神经网络的确切预先映射
An Analytical Approach to Compute the Exact Preimage of Feed-Forward Neural Networks
论文作者
论文摘要
神经网络是一种自动拟合过于复杂而无法手动描述的功能的便捷方法。这种方法的缺点是，它导致在不了解内部发生的事情的情况下构建一个黑框。查找预先形象将有助于更好地了解这种神经网络如何以及为什么给出这样的输出。由于大多数神经网络都是非注入函数，因此通常不可能仅通过数值方式完全计算它。这项研究的目的是提供一种方法，以计算任何具有线性或分段线性激活函数的馈送前馈神经网络的精确预先形象。与其他方法相反，该方法没有为唯一的输出返回唯一的解决方案，而是在分析整个且精确的预先映射方面返回。
Neural networks are a convenient way to automatically fit functions that are too complex to be described by hand. The downside of this approach is that it leads to build a black-box without understanding what happened inside. Finding the preimage would help to better understand how and why such neural networks had given such outputs. Because most of the neural networks are noninjective function, it is often impossible to compute it entirely only by a numerical way. The point of this study is to give a method to compute the exact preimage of any Feed-Forward Neural Network with linear or piecewise linear activation functions for hidden layers. In contrast to other methods, this one is not returning a unique solution for a unique output but returns analytically the entire and exact preimage.
