Multiresolution-based bilinear recurrent neural network |
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Authors: | Dong-Chul Park |
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Affiliation: | (1) ICRL, Department of Information Engineering, Myong Ji University, Yong In, 449-723, Republic of Korea |
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Abstract: | A multiresolution-based bilinear recurrent neural network (MBLRNN) is proposed in this paper. The proposed MBLRNN is based
on the BLRNN that has robust abilities in modeling and predicting time series. The learning process is further improved by
using a multiresolution-based learning algorithm for training the BLRNN so as to make it more robust for the prediction of
time series data. The proposed MBLRNN is applied to the problems of network traffic prediction and electric load forecasting.
Experiments and results on both practical problems show that the proposed MBLRNN outperforms both the traditional multilayer
perceptron type neural network (MLPNN) and the BLRNN in the prediction accuracy.
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Keywords: | Wavelet transform Recurrent neural network Time series prediction Network traffic Load forecasting |
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