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Multiresolution-based bilinear recurrent neural network

机译：基于多分辨率的双线性递归神经网络

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摘要

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.
机译：提出了一种基于多分辨率的双线性递归神经网络（MBLRNN）。所提出的MBLRNN基于BLRNN，该模型在建模和预测时间序列方面具有强大的功能。通过使用基于多分辨率的学习算法来训练BLRNN，以使其对时间序列数据的预测更加健壮，可以进一步改善学习过程。提出的MBLRNN应用于网络流量预测和电力负荷预测问题。对这两个实际问题的实验和结果表明，所提出的MBLRNN在预测精度上优于传统的多层感知器型神经网络（MLPNN）和BLRNN。

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