Title :
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
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;
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
DOI :
10.1109/ICMLC.2003.1259702
