Approximation by ridge functions and neural networks with a bounded number of neurons

被引：12

作者：

Ismailov, Vugar E. ^{[1
]}

机构：

[1] Natl Acad Sci Azerbaijan, Inst Math & Mech, AZ-1141 Baku, Azerbaijan

来源：

APPLICABLE ANALYSIS | 2015年 / 94卷 / 11期

关键词：

neural network; MLP model; activation function; weight; ridge function; extremal element; path; orbit; 41A30; 41A63; 68T05; 92B20; MULTILAYER FEEDFORWARD NETWORKS; RECONSTRUCTION; WEIGHTS; CAPABILITIES; OPTIMIZATION; DIRECTIONS;

D O I：

10.1080/00036811.2014.979809

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we consider the problem of approximation of continuous multivariate functions by neural networks with a bounded number of neurons in hidden layers. We prove the existence of single-hidden-layer networks with bounded number of neurons, which have approximation capabilities not worse than those of networks with arbitrarily many neurons. Our analysis is based on the properties of ridge functions.

引用

页码：2245 / 2260

页数：16

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