DocumentCode
2699541
Title
Ammunition storage reliability forecasting based on radial basis function neural network
Author
Liu, Jiang ; Ling, Dan ; Wang, Song
Author_Institution
Unit 78618, PLA, Chengdu, China
fYear
2012
fDate
15-18 June 2012
Firstpage
599
Lastpage
602
Abstract
In order to forecast ammunition storage reliability better, the paper researched a forecasting method based on neural network which is with the ability of actualizing multi-nonlinear mapping from input to output, and discussed steps of forecasting based on radial basis function (RBF) network. The storage reliability of one new-style ammunition is forecasted based on RBF network. The results show that RBF is suitable for ammunition storage reliability forecasting, and the precision of the train goal is better by using RBF network than by using BP network. RBF network is more suitable for dealing with this problem.
Keywords
military computing; radial basis function networks; reliability; weapons; RBF network; ammunition storage reliability forecasting; multinonlinear mapping; new-style ammunition; radial basis function neural network; Forecasting; Missiles; Neurons; Radial basis function networks; Reliability; Training; Transfer functions; ammunition storage reliability; forecasting; radial basis function neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE), 2012 International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4673-0786-4
Type
conf
DOI
10.1109/ICQR2MSE.2012.6246305
Filename
6246305
Link To Document