Multiscale BiLinear Recurrent Neural Network with an adaptive learning algorithm

In this paper, a wavelet-based neural network architecture called the Multiscale BiLinear Recurrent Neural Network with an adaptive learning algorithm (M-BLRNN(AL)) is proposed. The proposed M-BLRNN(AL) is formulated by a combination of several BiLinear Recurrent Neural Network (BLRNN) models in which each model is employed for predicting the signal at a certain level obtained by a wavelet transform. The learning process is further improved by applying an adaptive learning algorithm at each resolution level. The proposed MBLRNN(AL) is applied to the long-term prediction of MPEG VBR video traffic data. Experiments and results on several MPEG data sets show that the proposed M-BLRNN(AL) outperforms the traditional Multi-Layer Perceptron Type Neural Network (MLPNN), the BLRNN, and the original M-BLRNN in terms of the normalized mean square error (NMSE).