A comparison between Kriging and radial basis function networks for nonlinear prediction

被引：0
|
作者：
Costa, JP [1 ]
Pronzato, L [1 ]
Thierry, E [1 ]
机构：
[1] CNRS, UNSA, Lab 13S, F-06410 Biot, France
来源：
PROCEEDINGS OF THE IEEE-EURASIP WORKSHOP ON NONLINEAR SIGNAL AND IMAGE PROCESSING (NSIP'99) | 1999年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Predictions by Kriging and radial basis function (RBF) networks with gaussian Kernels are compared. Kriging is a semi-parametric approach that does not rely on any specific model structure, which makes it much more flexible than approaches based on parametric behavioural models. On the other hand, accurate predictions are obtained for short training sequences, which is not the case for nonparametric prediction methods based on neural networks. Examples are presented to illustrate the effectiveness of the method.
引用
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页码：726 / 730
页数：5
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