Feedforward wavelet neural network and multi-variable functional approximation

Authors:
Jing Zhao;Wang Chen;Jianhua Luo
Affiliations:
Biomedical Engineering Department, School of Life Science and Technology, Shanghai Jiaotong University, Shanghai, P.R. China;Department of Automation, Logistical Engineering University, Chongqing, P.R. China;Biomedical Engineering Department, School of Life Science and Technology, Shanghai Jiaotong University, Shanghai, P.R. China
Venue:
CIS'04 Proceedings of the First international conference on Computational and Information Science
Year:
2004

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Abstract

In this paper, a novel WNN, multi-input and multi-output feedforward wavelet neural network is constructed. In the hidden layer, wavelet basis functions are used as activate function instead of the sigmoid function of feedforward network. The training formulas based on BP algorithm are mathematically derived and training algorithm is presented. A numerical experiment is given to validate the application of this wavelet neural network in multi-variable functional approximation.