Approximation capability analysis of parallel process neural network with application to aircraft engine health condition monitoring

被引：0

作者：

Ding, Gang ^{[1
]}

Zhong, Shisheng ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Mech Engn, Harbin 150001, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 3, PROCEEDINGS | 2007年 / 4493卷

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Parallel process neural network (PPNN) is a novel spatio-temporal artificial neural network. The approximation capability analysis is very important for the PPNN to enhance its adaptability to time series prediction. The approximation capability of the PPNN is analyzed in this paper, and it can be proved that the PPNN can approximate any continuous functional to any degree of accuracy. Finally, the PPNN is utilized to predict the iron concentration of the lubricating oil in the aircraft engine health condition monitoring to highlight the approximation capability of the PPNN, and the application test results also indicate that the PPNN can be used as a well predictive maintenance tool in the aircraft engine condition monitoring.

引用

页码：66 / +

页数：3

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