Center selection for RBF neural network in prediction of nonlinear time series

被引：2

作者：

Lu, YH ^{[1
]}

Wu, CG ^{[1
]}

Liang, YC ^{[1
]}

机构：

[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China

来源：

2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年

关键词：

radial basis function; neural network; time series; orthogonal least square;

D O I：

10.1109/ICMLC.2003.1259702

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a new method for center selection of radial basis function (RBF) neural network. The proposed method endows a parallel quality on the process of center selection and takes advantage of the time sequential relation among time series data. Stock price prediction simulation shows that, compared with hard c-means (HCM) and orthogonal least square (OLS) RBF neural network, our method has not only better training and testing precisions, but also better generalization ability.

引用

页码：1355 / 1359

页数：5

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