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
收藏
页码：1355 / 1359
页数：5
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