Narrow Results

A prediction scheme for Ethernet traffic data using a Multiscale-Bilinear Recurrent Neural Network with Adaptive Learning (M-BRNN-AL) is proposed and presented in this paper. The proposed predictor integrates an M-BRNN and an AL algorithm. In M-BRNN, the wavelet transform is employed to decompose the original traffic signals into several simple traffic signals. A BRNN is then used to predict each decomposed traffic signal. An AL algorithm is also applied in order to improve the learning process at each resolution level in M-BRNN-AL. Experiments and results on a set of Ethernet network traffic predictions show that the proposed scheme converges faster and archives better prediction performance than the other conventional models such as the Multi-layer Perception Type Neural Network, BRNN, and the original M-BRNN in terms of the normalized mean square error.