title: Wavelet basis function neural networks for sequential learning.
abstract: In this letter, we develop the wavelet basis function neural networks (WBFNNs). It is analogous to radial basis function neural networks (RBFNNs) and to wavelet neural networks (WNNs). In WBFNNs, both the scaling function and the wavelet function of a multiresolution approximation (MRA) are adopted as the basis for approximating functions. A sequential learning algorithm for WBFNNs is presented and compared to the sequential learning algorithm of RBFNNs. Experimental results show that WBFNNs have better generalization property and require shorter training time than RBFNNs.
authors.affiliation: Department of Electrical and Computer Engineering, University of Illinois, Chicago, IL 60607-7053, USA. njin@ece.uic.edu
authors.forename: Ning
authors.initials: N
authors.lastname: Jin
authors.forename: Derong
authors.initials: D
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D000818:Animals; D006801:Humans; D016247:Information Storage and Retrieval; D016571:Neural Networks, Computer; D012691:Serial Learning / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted