Shallow neural network approximation in mixed Sobolev spaces

2026-09-04

AI Summary

研究了用含n个神经元的浅层神经网络在混合Sobolev空间中的最佳L2逼近问题，通过Fourier-block原理分析了不同激活函数下的逼近阶数。

Abstract

We investigate the best $L_2$ approximation of mixed Sobolev spaces by shallow neural networks with $n$ neurons and general activation functions. We first establish an activation-independent Fourier-block principle: if an activation has univariate approximation order $ρ$ in the sense of the Fourier-block property, then the global approximation rate has algebraic order $\min\{α,ρ\}$ for target functions of mixed smoothness $α$, up to explicit logarithmic factors. To verify this property for concrete activations, we introduce a structured univariate approximation condition that implies the Fourier-block property with explicit parameters. For $\mathrm{ReLU}^k$, a matching algebraic lower bound identifies $\min\{α,k+1\}$ as the optimal algebraic approximation exponent in any dimension, up to logarithmic factors in the upper bound. The framework also yields the exponent $\min\{α,k+1\}$ for cardinal B-splines and soft-$\mathrm{ReLU}^k$, and the full mixed-smoothness exponent $α$ for ELU and cosine activations, again up to logarithmic~factors.

Problem

• shallow neural networks
• mixed Sobolev spaces
• approximation rate
• activation functions
• Fourier-block property

Innovation

• Fourier-block principle
• mixed Sobolev spaces
• shallow neural networks
• ReLU^k
• approximation rate