A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
Predictive head movement tracking using a Kalman filter
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wavelet-based combined signal filtering and prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Prediction of noisy chaotic time series using an optimal radial basis function neural network
IEEE Transactions on Neural Networks
Complex-bilinear recurrent neural network for equalization of a digital satellite channel
IEEE Transactions on Neural Networks
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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 Multi-Layer Perceptron Type Neural Network (MLPNN) and the BLRNN in terms of the normalized mean square error (NMSE).