On simultaneous approximations by radial basis function neural networks

被引：30

作者：

Li, X ^{[1
]}

机构：

[1] Univ Nevada, Dept Math Sci, Las Vegas, NV 89154 USA

来源：

APPLIED MATHEMATICS AND COMPUTATION | 1998年 / 95卷 / 01期

关键词：

radial basis functions; neural networks; simultaneous approximations;

D O I：

10.1016/S0096-3003(97)10089-3

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

It is shown in this paper by a constructive method that any multivariate function and all its existing derivatives can be simultaneously approximated by a radial basis function (RBF) neural network, where the assumptions on the RBFs are relatively mild. By considering the translations of RBFs, it is proved that an O(m(-1/2)) order of simultaneous approximations to a certain class of functions can be achieved by using a translation network with just m neurons. (C) 1998 Elsevier Science Inc. All rights reserved.

引用

页码：75 / 89

页数：15

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