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
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;
Conference_Titel :
Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE), 2012 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4673-0786-4
DOI :
10.1109/ICQR2MSE.2012.6246305