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Multiresolution-based bilinear recurrent neural network

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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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Authors and Affiliations

  1. ICRL, Department of Information Engineering, Myong Ji University, Yong In, 449-723, Republic of Korea

    Dong-Chul Park

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  1. Dong-Chul Park
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Correspondence to Dong-Chul Park.

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Park, DC. Multiresolution-based bilinear recurrent neural network. Knowl Inf Syst 19, 235–248 (2009). https://doi.org/10.1007/s10115-008-0155-1

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  • DOI: https://doi.org/10.1007/s10115-008-0155-1

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