Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
Expert Systems with Applications: An International Journal
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
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In this paper, a novel hybrid Radial Basis Function Neural Network (RBF---NN) ensemble model is proposed for rainfall forecasting based on Kernel Partial Least Squares Regression (K---PLSR). In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input to the RBF---NN models of different kernel function, and then various single RBF---NN predictors are produced. Finally, K---PLSR is used for ensemble of the prediction purpose. Our findings reveal that the K---PLSR ensemble model can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy.