THE BEST APPROXIMATION TO C-2 FUNCTIONS AND ITS ERROR-BOUNDS USING REGULAR-CENTER GUASSIAN NETWORKS

被引：15

作者：

LIU, BF

SI, J

机构：

[1] The Department of Electrical Engineering, Arizona State University, Tempe

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 1994年 / 5卷 / 05期

关键词：

D O I：

10.1109/72.317739

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Gaussian neural networks are considered to approximate any C2 function with support on the unit hypercube I(m) = [0, 1]m in the sense of best approximation. An upper bound (O(N-2)) of the approximation error is obtained in the present letter for a Gaussian network having N(m) hidden neurons with centers defined on a regular mesh in I(m).

引用

页码：845 / 847

页数：3

共 20 条

[1] UNIVERSAL APPROXIMATION BOUNDS FOR SUPERPOSITIONS OF A SIGMOIDAL FUNCTION [J].