[1]孟雅俊,黄士涛,姬中华..改进的RBF网络训练方法在故障诊断中的应用[J].郑州大学学报(工学版),2005,26(04):89-92.[doi:10.3969/j.issn.1671-6833.2005.04.022]
 Meng Yajun,HUANG Shitao,Ji Zhonghua.Application of improved RBF network training method in fault diagnosis[J].Journal of Zhengzhou University (Engineering Science),2005,26(04):89-92.[doi:10.3969/j.issn.1671-6833.2005.04.022]
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改进的RBF网络训练方法在故障诊断中的应用()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
26
期数:
2005年04期
页码:
89-92
栏目:
出版日期:
1900-01-01

文章信息/Info

Title:
Application of improved RBF network training method in fault diagnosis
作者:
孟雅俊黄士涛姬中华.
郑州大学机械工程学院,河南,郑州,450002, 郑州大学机械工程学院,河南,郑州,450002, 郑州大学机械工程学院,河南,郑州,450002
Author(s):
Meng Yajun; HUANG Shitao; Ji Zhonghua
关键词:
RBF网络 正交最小二乘法 输入聚类法 输出-输入聚类法
Keywords:
DOI:
10.3969/j.issn.1671-6833.2005.04.022
文献标志码:
A
摘要:
目前已有的几种RBF网络训练方法对于含有随机噪声的复杂样本训练速度过慢且分类性能不稳定,依据相对熵最小原理,提出了一种改进的RBF网络训练方法--输出-输入聚类法.利用此方法对旋转机械故障样本进行训练,并与其它方法进行了比较,结果表明,此训练方法用时短,网络结构简单,受噪声影响小.将所创建网络应用于故障诊断,实例表明,此方法训练的网络诊断结果准确,在故障诊断中具有良好的应用前景.
Abstract:
At present, several existing RBF network training methods are proposed for complex samples with random noise training speed is too slow and the classification performance is unstable, according to the principle of minimum relative entropy, an improved RBF network training method-output-input clustering method is proposed. This method is used to train the rotating machinery fault samples and compare with other methods, and the results show that the training method takes a short time, has a simple network structure, and is less affected by noise. The created network is applied to fault diagnosis, and the examples show that the network diagnosis results trained by this method are accurate and have good application prospects in fault diagnosis.

更新日期/Last Update: 1900-01-01