Feedforward wavelet neural network and multi-variable functional approximation

被引：0
|
作者：
Zhao, J [1 ]
Chen, W
Luo, JH
机构：
[1] Shanghai Jiao Tong Univ, Sch Life Sci & Technol, Dept Biomed Engn, Shanghai 200240, Peoples R China
[2] Logist Engn Univ, Dept Automat, Chongqing 400016, Peoples R China
来源：
COMPUTATIONAL AND INFORMATION SCIENCE, PROCEEDINGS | 2004年 / 3314卷
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 multivariable functional approximation.
引用
收藏
页码：32 / 37
页数：6
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