Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
Applied Stochastic Models in Business and Industry
Expert Systems with Applications: An International Journal
Modeling meteorological prediction using particle swarm optimization and neural network ensemble
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
An application of artificial neural networks for rainfall forecasting
Mathematical and Computer Modelling: An International Journal
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
A semiparametric regression ensemble model for rainfall forecasting based on RBF neural network
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
A novel nonlinear neural network ensemble model using K-PLSR for rainfall forecasting
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Semi-parametric smoothing regression model based on GA for financial time series forecasting
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
International Journal of Applied Evolutionary Computation
Hybrid PSO and GA for neural network evolutionary in monthly rainfall forecasting
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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In this study, we propose a novel nonparametric regression (NR) ensemble rainfall forecasting model integrating generalized particle swarm optimization (PSO) with artificial neural network (ANN). First of all, the PSO algorithm is used to evolve neural network architecture and connection weights. The evolved neural network architecture and connection weights are input into a new neural network.The new neural network is trained using back-propagation (BP) algorithm, generating different individual neural network. Then, the principal component analysis (PCA) technology is adopted to extract ensemble members. Finally, the NR is used for nonlinear ensemble model. Empirical results obtained reveal that the prediction by using the NR ensemble model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. For illustration and testing reveal that the NR ensemble model proposed can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy and improving prediction quality further.