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Proceedings Paper

Multiresolution dynamic predictor based on neural networks
Author(s): Fu-Chiang Tsui; Ching-Chung Li; Mingui Sun; Robert J. Sclabassi
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Paper Abstract

We present a multiresolution dynamic predictor (MDP) based on neural networks for multi- step prediction of a time series. The MDP utilizes the discrete biorthogonal wavelet transform to compute wavelet coefficients at several scale levels and recurrent neural networks (RNNs) to form a set of dynamic nonlinear models for prediction of the time series. By employing RNNs in wavelet coefficient space, the MDP is capable of predicting a time series for both the long-term (with coarse resolution) and short-term (with fine resolution). Experimental results have demonstrated the effectiveness of the MDP for multi-step prediction of intracranial pressure (ICP) recorded from head-trauma patients. This approach has applicability to quasi- stationary signals and is suitable for on-line computation.

Paper Details

Date Published: 22 March 1996
PDF: 11 pages
Proc. SPIE 2762, Wavelet Applications III, (22 March 1996); doi: 10.1117/12.236039
Show Author Affiliations
Fu-Chiang Tsui, Univ. of Pittsburgh (United States)
Ching-Chung Li, Univ. of Pittsburgh (United States)
Mingui Sun, Univ. of Pittsburgh (United States)
Robert J. Sclabassi, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 2762:
Wavelet Applications III
Harold H. Szu, Editor(s)

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