Runtime Prediction of Armored Vehicle Engine Based on Neural Network
To a great extent, engines runtime of armored vehicle reflects its technical state. By estimating engines runtime, the remaining service life can be forecasted. In this paper, the virtues of neural network prediction are introduced. Aiming at a certain armored vehicle engine, the BP neural network regression prediction model is constructed based on four typical state parameters including cylinder compression pressures, acceleration time, deceleration time and supply fuel advance angle. The model is trained and validated by samples data. The prediction results indicate that the model is effective and feasible. At last, two prediction problems that need to be studied hard are proposed.
runtime BP neural network prediction armred vehicle engine
Chunliang Chen Yanhua cao Hongbing Ye Yongjun Song
Department of Technical Support Engineering Academy of Armored Forces Engineering Beijing, China Graduates Administration Unit Naval Aeronautical and Astronautical University Yantai, Shandong Provi
国际会议
重庆
英文
350-353
2011-01-21（万方平台首次上网日期，不代表论文的发表时间）