An Investigation of Interpolation Scheme Based on Indirect Radial Basis Neural Networks

被引：0
|
作者：
Sun, Haitao [1 ]
机构：
[1] Zhejiang A&F Univ, Sch Landscape & Architecture, Hangzhou 311300, Zhejiang, Peoples R China
来源：
VIBRATION, STRUCTURAL ENGINEERING AND MEASUREMENT II, PTS 1-3 | 2012年 / 226-228卷
关键词：
Numerical Method; Interpolation Scheme; Indirect Radial Basis Neural Networks;
D O I：
10.4028/www.scientific.net/AMM.226-228.2181
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
An indirect radial basis neural network (IRBNN) is proposed for improving the accuracy of the approximated functions. The IRBNN is constructed by new prompted functions generated from the Nth order derivative of the approximated function. In this way, high accuracy derivatives in different order can be obtained, so that more accuracy of the numerical results would be given while the IRBNN is employed for creating approximated functions in numerical methods. Numerical results through applications in elasticity show the effectiveness and accuracy of the IRBNN method.
引用
收藏
页码：2181 / 2188
页数：8
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