Relaxed conditions for radial-basis function networks to be universal approximators

被引:76
作者
Liao, Y [1 ]
Fang, SC [1 ]
Nuttle, HLW [1 ]
机构
[1] N Carolina State Univ, Raleigh, NC 27695 USA
关键词
universal approximation; radial-basis function networks;
D O I
10.1016/S0893-6080(02)00227-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we investigate the universal approximation property of Radial Basis Function (RBF) networks. We show that RBFs are not required to be integrable for the REF networks to be universal approximators. Instead, RBF networks can uniformly approximate any continuous function on a compact set provided that the radial basis activation function is continuous almost everywhere, locally essentially bounded, and not a polynomial. The approximation in L-p(mu)(1 less than or equal to p < infinity) space is also discussed. Some experimental results are reported to illustrate our findings. (C) 2003 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:1019 / 1028
页数:10
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