Wavelet Basis Function Neural Networks
Ning Jin, Derong Liu, Zhongyu Pang, Ting Huang · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · 2007
In this paper, a new kind of neural networks for sequential learning is proposed, which are called wavelet basis function neural networks (WBFNNs). They are 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 for RBFNNs. Experimental results show that WBFNNs has better generalization property and require shorter training time than RBFNNs.