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22 March 1996 Neural networks adaptive wavelets for predictions of the northeastern Brazil monthly rainfall anomalies time series
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Abstract
Neural networks were used to predict the anomalies of the time series of monthly rainfall of the Northeastern Region of Brazil. The forecasts made using a feedforward network with backpropagation algorithm from the original data were not satisfactory. We have therefore tried to combine two advanced methods, Wavelet Transform and Neural networks. Three more types of neural networks were used. The selected neural networks include the Time Delay Neural Networks (TDNN), Radial Basis Functions network and Neural Network Adaptive Wavelet. All networks were implemented in neural network simulator SNNS. The Neural Network Adaptive Wavelet was implemented by changing the standard sigmoidal nonlinearities to wavelet nonlinearities in the neurons. We compare the results obtained with unfiltered and filtered data. Using data obtained by filtering the wavelet transform coefficients significantly improved the results for all networks. The combination of TDNN with wavelet filtered data gave the best results.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Weigang Li, Leonardo Deane Sa, G. S.S. Prasad, A. G. Nowosad, Mauricio Bolzan, and E. S. M. Chiang "Neural networks adaptive wavelets for predictions of the northeastern Brazil monthly rainfall anomalies time series", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235908
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