Title :
Wavelet networks for functional learning
Author :
Zhang, Jun ; Walter, GiEbert G.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Wisconsin Univ., Milwaukee, WI, USA
Abstract :
A wavelet-based neural network is described. The network is similar to the radial basis function (RBF) network, except that the RBF´s are replaced by orthonormal scaling functions. It has been shown that the wavelet network has universal and L2 approximation properties and is a consistent function estimator. Convergence rates, which avoid the “curse of dimensionality,” are obtained for certain function classes. The network also compared favorably to the MLP and RBF networks in the experiments
Keywords :
convergence; feedforward neural nets; learning (artificial intelligence); wavelet transforms; L2 approximation properties; consistent function estimator; convergence rates; functional learning; neural network; orthonormal scaling functions; radial basis function network; universal properties; wavelet networks; Convergence; Mathematics; Mean square error methods; Mercury (metals); Multilayer perceptrons; Neural networks; Radial basis function networks; Training data;
Conference_Titel :
Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
Conference_Location :
Alexandria, VA
Print_ISBN :
0-7803-2761-6
DOI :
10.1109/WITS.1994.513931
