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Updated: Jul 19, 2025

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使用深度 ReLU 网络对平滑函数的近似计算
Linhao Song1, Ying Liu2, Jun Fan3
1School of Mathematical Science, Beihang University, Beijing, China; School of Data Science, City University of Hong Kong, Kowloon, Hong Kong.
概括
深度神经网络,特别是功能深度ReLU网络,对非线性连续函数的近似能力有所提高. 这项研究为ReLU网络提供了新的理论分析,为各种功能空间提供了更好的近似率.
科学领域:
- 应用数学 应用数学 应用数学
- 机器学习理论机器学习理论
- 数字分析 数字分析
背景情况:
- 深度神经网络用于近似非线性连续函数.
- 现有的理论分析存在局限性,特别是在修正线性单位 (ReLU) 激活函数方面.
- 需要对功能深度ReLU网络的近似功率有更好的理论理解.
研究的目的:
- 为了研究功能深度ReLU网络的近似功率.
- 分析具有受限连续性模块的连续函数和具有更高阶Fréchet导数的连续函数的近似能力.
- 开发新的网络结构,用于功能近似的特征提取.
主要方法:
- 提出一种新型的功能网络架构,设计用于更高阶的流性特征提取.
- 根据网络深度,宽度和重量的总数,推导出数量近似率.
- 在霍尔德和分析函数空间上分析近似误差.
主要成果:
- 在霍尔德空间的单位球上实现了对数近似率.
- 建立了分析函数空间的近多项式近似率.
- 与现有文献相比,证明了更好的近似结果,特别是对于ReLU网络.
结论:
- 功能深度ReLU网络对于非线性连续函数具有显著的近似能力.
- 拟议的网络结构和理论分析为理解功能近似的深度学习提供了坚实的基础.
- 这项工作推进了对深层ReLU网络在近似复杂数学函数方面的理论理解.
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