Paper
22 March 1996 Multiresolution dynamic predictor based on neural networks
Fu-Chiang Tsui, Ching-Chung Li, Mingui Sun, Robert J. Sclabassi
Author Affiliations +
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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fu-Chiang Tsui, Ching-Chung Li, Mingui Sun, and Robert J. Sclabassi "Multiresolution dynamic predictor based on neural networks", Proc. SPIE 2762, Wavelet Applications III, (22 March 1996); https://doi.org/10.1117/12.236039
Lens.org Logo
CITATIONS
Cited by 4 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Wavelets

Neural networks

Wavelet transforms

Solids

Switches

Autoregressive models

Silicon

Back to Top