Wavelet Basis Function Neural Networks

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...

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Bibliographic Details
Main Authors: Ning Jin, Derong Liu, Zhongyu Pang, Ting Huang
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Continuous wavelet transforms
Joining processes
Multi-layer neural network
Multiresolution analysis
Neural networks
Neurons
Radial basis function networks
USA Councils
Wavelet transforms
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Summary: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.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2007.4371007