Abstract
A Multiresolution-based BiLinear Recurrent Neural Network (MBLRNN) is proposed in this paper. The proposed M-BLRNN is based on the BLRNN that has been proven to have 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 long-term prediction of the time series. The proposed M-BLRNN is applied to long-term prediction of network traffic. Experiments and results on Ethernet network traffic data show that the proposed M-BLRNN outperforms both the traditional MultiLayer Perceptron Type Neural Network (MLPNN) and the BLRNN in terms of the normalized mean square error (NMSE).
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Min, BJ., Park, DC., Choi, HS. (2007). Multiresolution-Based BiLinear Recurrent Neural Network. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_23
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DOI: https://doi.org/10.1007/978-3-540-71701-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
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