Prediction of Aero-Engine Wear Based on Genetic Algorithms and BP Neural Network

被引：0
|
作者：
Zhang, Zhen [1 ,2 ]
Shi, Chao-Yang [3 ]
机构：
[1] Zhengzhou Univ Aeronaut, Zhengzhou, Peoples R China
[2] Collaborat Innovat Ctr Aviat Econ Dev Henan Prov, Zhengzhou, Peoples R China
[3] Zhengzhou Yellow River Nursing Vocat Coll, Zhengzhou, Peoples R China
来源：
MANUFACTURING CONSTRUCTION AND ENERGY ENGINEERING: 2016 INTERNATIONAL CONFERENCE ON MANUFACTURING CONSTRUCTION AND ENERGY ENGINEERING | 2016年
关键词：
Aero-engine wear; BP neural network; genetic algorithms; fault prediction;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A prediction model based on the genetic algorithms and back propagation (BP) network by using genetic algorithm to train back propagation neural network and optimize network structure was presented. The aero-engine wear was estimated by the optimized network. The result of this approach was poly-regression model, which was compared with that of the simple BP network as well as Poly-regression model. The results indicate that BP neural network based on the genetic algorithms is superior to the simple BP network and the poly-regression model.
引用
收藏
页码：463 / 468
页数：6
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