Research on Performance Degradation Modeling for Machine Gun’s Barrel Based on FOAGRNN

DOI

10.2991/icmmcce-15.2015.491How to use a DOI?

Keywords

fruit fly optimization algorithm; general regression neural network; performance degradation; forecast.

Abstract

A method to establish performance degradation model for barrel based on general regression neural network with fruit fly optimization algorithm (FOAGRNN) was proposed. It took the muzzle velocity reduction as performance degradation feature with the increase in the number of shooting ammunition quantity under various working conditions, based on the performance degradation experimental data of barrel. The forecasting results were basically consistent with experimental results, which proved the feasibility of the method.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yanfeng Cao
AU  - Cheng Xu
PY  - 2015/12
DA  - 2015/12
TI  - Research on Performance Degradation Modeling for Machine Gun’s Barrel Based on FOAGRNN
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.491
DO  - 10.2991/icmmcce-15.2015.491
ID  - Cao2015/12
ER  -