Wavelet basis function neural networks for sequential learning.
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.
Jin
Ning
N
Department of Electrical and Computer Engineering, University of Illinois, Chicago, IL 60607-7053, USA. njin@ece.uic.edu
Liu
Derong
D
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