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- W2365202822 abstract "A novel nonparametric regression neural network ensemble model for rainfall forecasting.Firstly,the evolving neural network architecture and beginning connection weights based on particle swarm optimization algorithms are presented to produces the better net-work architecture and beginning connection weights,trains again the traditional back propagation by training samples.Secondly,the principal component analysis technique is used to select the appropriate ensemble members.Finally,the nonparametric regression model is used to neural network ensemble based on Gaussian kernel function estimation with variable band-with.For further illustration,the rainfall data are used for testing of the Guangxi three different regional in May and June.The result shows that the three regional neural network ensemble models are better than T213 in the corresponding period in day-by-day forecast." @default.
- W2365202822 created "2016-06-24" @default.
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- W2365202822 date "2008-01-01" @default.
- W2365202822 modified "2023-09-23" @default.
- W2365202822 title "Neural network ensemble model for rainfall forecasting based on nonparametric regression" @default.
- W2365202822 hasPublicationYear "2008" @default.
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