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Multiresolution-based bilinear recurrent neural network

Published: 01 May 2009 Publication History

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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  • (2018)A general insight into the effect of neuron structure on classificationKnowledge and Information Systems10.5555/3225656.322594030:1(135-154)Online publication date: 29-Dec-2018
  • (2018)Combustion optimization of a coal-fired boiler with double linear fast learning networkSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1486-320:1(149-156)Online publication date: 30-Dec-2018

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Published In

cover image Knowledge and Information Systems
Knowledge and Information Systems  Volume 19, Issue 2
May 2009
132 pages

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 May 2009

Author Tags

  1. Load forecasting
  2. Network traffic
  3. Recurrent neural network
  4. Time series prediction
  5. Wavelet transform

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View all
  • (2018)A general insight into the effect of neuron structure on classificationKnowledge and Information Systems10.5555/3225656.322594030:1(135-154)Online publication date: 29-Dec-2018
  • (2018)Combustion optimization of a coal-fired boiler with double linear fast learning networkSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1486-320:1(149-156)Online publication date: 30-Dec-2018

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