DocumentCode :
1559048
Title :
Evolving mixture of experts for nonlinear time series modelling and prediction
Author :
Sun-Gi Hong ; Sang-Keon Oh ; Min-Soeng Kim ; Ju-Jang Lee
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Taejon, South Korea
Volume :
38
Issue :
1
fYear :
2002
Firstpage :
34
Lastpage :
35
Abstract :
The evolutionary structure optimisation (ESO) method for Gaussian radial basis function (RBF) networks has already been presented by the authors. Here, they improve the ESO method in its mutation operator and apply it to a mixture of experts (ME) for modelling and predicting nonlinear time series. The ME implementation provides much better generalisation performance with fewer network parameters, compared to the Gaussian RBF networks.
Keywords :
evolutionary computation; generalisation (artificial intelligence); prediction theory; radial basis function networks; time series; ESO method; Gaussian radial basis function networks; evolutionary structure optimisation; generalisation performance; mixture of experts; mutation operator; network parameters; nonlinear prediction; nonlinear time series modelling;
fLanguage :
English
Journal_Title :
Electronics Letters
Publisher :
iet
ISSN :
0013-5194
Type :
jour
DOI :
10.1049/el:20020010
Filename :
977544
Link To Document :
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