Performance comparison among nonparametric probability density estimator, radial basis function, and adaptive wavelet transform neural networks

Weigang Li; Harold H. Szu; Joao Fernando Marar; Leonardo Deane Sa; Edson C. B. Carvalho Filho

doi:10.1117/12.271709

3 April 1997 Performance comparison among nonparametric probability density estimator, radial basis function, and adaptive wavelet transform neural networks

Weigang Li, Harold H. Szu, Joao Fernando Marar, Leonardo Deane Sa, Edson C. B. Carvalho Filho

Author Affiliations +

Proceedings Volume 3078, Wavelet Applications IV; (1997) https://doi.org/10.1117/12.271709
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Weigang Li, Harold H. Szu, Joao Fernando Marar, Leonardo Deane Sa, and Edson C. B. Carvalho Filho "Performance comparison among nonparametric probability density estimator, radial basis function, and adaptive wavelet transform neural networks", Proc. SPIE 3078, Wavelet Applications IV, (3 April 1997); https://doi.org/10.1117/12.271709

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