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 M-BLRNN(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 MultiLayer Perceptron Type Neural Network (MLPNN), the BLRNN, and the original M-BLRNN in terms of the normalized mean square error (NMSE).

[1]  Amir F. Atiya,et al.  Multi-step-ahead prediction using dynamic recurrent neural networks , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[2]  Dong-Chul Park,et al.  Bilinear recurrent neural network , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[3]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Abdelnaser Mohammad Adas.  Using adaptive linear prediction to support real-time VBR video under RCBR network service model , 1998, TNET.

[5]  Dong-Chul Park,et al.  Complex-bilinear recurrent neural network for equalization of a digital satellite channel , 2002, IEEE Trans. Neural Networks.

[6]  Yao Liang,et al.  Multiresolution learning paradigm and signal prediction , 1997, IEEE Trans. Signal Process..

[7]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

[8]  Mitsuru Nomura,et al.  Basic characteristics of variable rate video coding in ATM environment , 1989, IEEE J. Sel. Areas Commun..

[9]  Javier R. Movellan,et al.  Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks , 1991, IEEE Trans. Syst. Man Cybern..

[10]  Amir F. Atiya,et al.  Prediction of MPEG-coded video source traffic using recurrent neural networks , 2003, IEEE Trans. Signal Process..