Multiscale BiLinear Recurrent Neural Network with an Adaptive Learning Algorithm

  • Byung-Jae Min
  • Chung Nguyen Tran
  • Dong-Chul Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


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).


Recurrent Neural Network Resolution Level Adaptive Learning Asynchronous Transfer Mode Normalize Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Byung-Jae Min
    • 1
  • Chung Nguyen Tran
    • 1
  • Dong-Chul Park
    • 1
  1. 1.ICRL, Dept. of Information EngineeringMyong Ji UniversityKorea

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