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

Author

Lu, Ying-hua ; Wu, Chun-Guo ; Liang, Yan-chun

Author_Institution

Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China

Volume

3

fYear

2003

fDate

2-5 Nov. 2003

Firstpage

1355

Abstract

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.

Keywords

learning (artificial intelligence); least squares approximations; nonlinear systems; radial basis function networks; time series; center selection; hard c-means RBF neural network; neural network training; nonlinear time series prediction; orthogonal least square RBF neural network; radial basis function; stock price prediction; Computer science; Educational institutions; Electronic mail; Intelligent networks; Least squares approximation; Least squares methods; Neural networks; Predictive models; Reduced order systems; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2003 International Conference on

Print_ISBN

0-7803-8131-9

Type

conf

DOI

10.1109/ICMLC.2003.1259702

Filename

1259702