Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks

O4; In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear timeseries, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-meansclustering is very suitable. In order to increase the precision we introduce a...

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Bibliographic Details
Published in:理论物理通讯(英文版) 2003, Vol.40 (8), p.165-168
Main Authors: ZHENG Xin, CHEN Tian-Lun
Format: Article
Language:English
Online Access:Get full text
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Summary:O4; In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear timeseries, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-meansclustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from thelocal minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glassequation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting resultsare obtained.
ISSN:0253-6102