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对于科罗博夫函数的近最佳深度神经网络近似,与Lp和H1规范相比
1Department of Mathematics, The Pennsylvania State University, McAllister Building, Pollock Rd, State College, 16802, PA, USA.
这项研究揭示了对接近科罗博夫函数的深度神经网络的最佳近似率. 结果显示超级融合,优于现有的连续函数近似方法.
科学领域:
- 机器学习 机器学习
- 数字分析 数字分析
- 函数近似函数的近似方法
背景情况:
- 深度神经网络是功能近似的强大工具.
- 了解它们的近似能力,特别是对于像科罗博夫函数这样的复杂函数类,至关重要.
- 现有的研究往往提供了不对称的边界或专注于特定的网络架构.
研究的目的:
- 通过使用深度神经网络来推导Korobov函数的最佳近似率.
- 提供关于网络宽度和深度的近似精度的非对称边界.
- 分析收行为并确定衍生率的近最佳性.
主要方法:
- 在高维超立方体上对深度神经网络的近似边界的导出.
- 关于L^p-规范和H^1-规范的近似分析.
- 应用VC维度参数来评估利率的最佳性.
主要成果:
- 建立了深度神经网络的非对称近似边界.
- 在接近率中表现出超级融合特征,超过现有基准.
- 量化了高维的科罗博夫函数的近似率.
结论:
- 对于深度神经网络来说,衍生的速率比当前的连续函数近似器提供了显著的改进.
- 超融合特性突出了深度神经网络在接近科罗博夫函数方面的效率.
- 虚拟货币维度分析证实,获得的近似率接近最佳.
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