PReLU：XOR 问题的另一种单层解决方案

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-17 DOI:arxiv-2409.10821

Rafael C. Pinto, Anderson R. Tavares

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引用次数: 0

摘要

本文证明了使用参数线性单元（PReLU）激活的单层神经网络可以解决 XOR 问题，而这是一个迄今为止一直被忽视的简单问题。我们将这一解决方案与多层感知器（MLP）和增长余弦单元（GCU）激活功能进行了比较，并解释了为什么 PReLU 能够实现这一功能。我们的结果表明，单层 PReLU 网络可以在更宽的学习率范围内实现 100% 的成功率，同时只使用三个可学习参数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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PReLU: Yet Another Single-Layer Solution to the XOR Problem

This paper demonstrates that a single-layer neural network using Parametric Rectified Linear Unit (PReLU) activation can solve the XOR problem, a simple fact that has been overlooked so far. We compare this solution to the multi-layer perceptron (MLP) and the Growing Cosine Unit (GCU) activation function and explain why PReLU enables this capability. Our results show that the single-layer PReLU network can achieve 100\% success rate in a wider range of learning rates while using only three learnable parameters.

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arXiv - CS - Neural and Evolutionary Computing

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