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
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;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:20020010