Share Email Print

Proceedings Paper

Performance comparison among nonparametric probability density estimator, radial basis function, and adaptive wavelet transform neural networks
Author(s): Weigang Li; Harold H. Szu; Joao Fernando Marar; Leonardo Deane Sa; Edson C. B. Carvalho Filho
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Wavelet shrinkage, radial basis function (RBF) have been studied for signal reconstructions. We first use these methods to approximate four specific functions which represent various spatially nonhomogeneous phenomena. Next, we apply these methods to analyze a time series of Paraguay River levels. From the preliminary experiments, we show that wavelet shrinkage was the best estimator. With similar result, secondly came AWTNN and lastly came RBF networks.

Paper Details

Date Published: 3 April 1997
PDF: 11 pages
Proc. SPIE 3078, Wavelet Applications IV, (3 April 1997); doi: 10.1117/12.271709
Show Author Affiliations
Weigang Li, Instituto Nacional de Pesquisas Espaciais (Brazil)
Harold H. Szu, Univ. of Southwestern Louisiana (United States)
Joao Fernando Marar, Univ. Estadual Paulista (Brazil)
Leonardo Deane Sa, Instituto Nacional de Pesquisas Espaciais (Brazil)
Edson C. B. Carvalho Filho, Univ. Federal de Pernambuco (Brazil)

Published in SPIE Proceedings Vol. 3078:
Wavelet Applications IV
Harold H. Szu, Editor(s)

© SPIE. Terms of Use
Back to Top