Application of General Regression Neural Network to vibration trend prediction of rotating machinery

被引：0
|
作者：
Feng, ZP [1 ]
Chu, FL
Song, XG
机构：
[1] Tsinghua Univ, Dept Precis Instruments & Mechanol, Beijing 100084, Peoples R China
[2] Dalian Univ Technol, Dept Power Engn, Dalian 116024, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 2 | 2004年 / 3174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：
The General Regression Neural Network (GRNN) is briefly introduced. The BIC method for determining the order of Auto Regression (AR) model is employed to select the number of input neurons, and the Genetic Algorithm is applied to calculate the optimal smoothing parameter. The GRNN is used to predict the vibration time series of a large turbo-compressor, and its performance is compared with that of Radial Basis Function Neural Network (RBFNN), Back Propagation Neural Network (BPNN), and AR. It is indicated that the GRNN is more appropriate for the prediction of time series than the others, and is qualified even with sparse sample data.
引用
收藏
页码：767 / 772
页数：6
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