Multiresolution learning paradigm and signal prediction

Details Multiresolution learning paradigm and signal prediction Multiresolution learning paradigm and signal prediction

2005-09-20

2005-09-20

Hirl, Joseph P. (Department: 2121)

Data processing: artificial intelligence

Neural network

Learning method

C706S015000, C706S020000

Reexamination Certificate

active

06947915

ABSTRACT:
A neural network learning process provides a trained network that has good generalization ability for even highly nonlinear dynamic systems, and is trained with approximations of a signal obtained, each at a different respective resolution, using wavelet transformation. Approximations are used in order from low to high. The trained neural network is used to predict values. In a preferred embodiment of the invention, the trained neural network is used in predicting network traffic patterns.

REFERENCES:
patent: 5621861 (1997-04-01), Hayashi et al.
patent: 6285992 (2001-09-01), Kwasny et al.
Will E. Leland et al., On the Self-Similar Nature of Ethernet Ttraffic (Extended Version), 1994, IEEE, 1063-6692/94, 1-15.
Wing-Chung Chan et al, Transformation of Back-Propagation Networks in Multiresolution Learning, 1994, IEEE, 0-7803-1901-X/94, 290-294.
Yao Liang, Application of Multiresolution Learning to Time-Series Forecasting, 1997, 180-183.
Liang, et al.; Application of Multiresolution Learning to Time-Series Forecasting Journal; In Proceedings of Third Annual International Joint Conference on Information Sciences (JCIS); Mar. 1997; pp. 180-183; vol. 2; Research Triangle Park, NC, USA.

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