TY - JOUR
T1 - Prediction on surface temperature of missile based on adaptive regression algorithm
AU - Liu, Shu Han
AU - Zhu, Zhan Xia
N1 - Publisher Copyright:
© 2017, Editorial Board of Journal of Ballistics. All right reserved.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - When the missile flies at high speed, the surface temperature rises due to aerodynamic heating. In order to predict the surface temperature of missile, a prediction model of missile surface temperature based on adaptive regression algorithm was established according to the known physical model and test data. The model was used to predict the temperature data, which were compared with the experimental data. A sample of 55 individuals was selected, and the individual samples were arranged according to the time order. The first 50 individuals were used to train the prediction model, and then the remaining 5 individuals were used to test the prediction model. At the corresponding time of the first 50 individuals, the predicted value was reduced by 1. 24%, and the standard deviation was 1. 27%. At the corresponding time of the last 5 individuals, the predicted value was reduced by an average of 1. 42%, and the standard deviation was about 0. 16%. The results show that the surface-temperature prediction-model of missile based on adaptive regression algorithm has high precision to predict the surface temperature of missile, and achieves the purpose of predicting the surface temperature of missile.
AB - When the missile flies at high speed, the surface temperature rises due to aerodynamic heating. In order to predict the surface temperature of missile, a prediction model of missile surface temperature based on adaptive regression algorithm was established according to the known physical model and test data. The model was used to predict the temperature data, which were compared with the experimental data. A sample of 55 individuals was selected, and the individual samples were arranged according to the time order. The first 50 individuals were used to train the prediction model, and then the remaining 5 individuals were used to test the prediction model. At the corresponding time of the first 50 individuals, the predicted value was reduced by 1. 24%, and the standard deviation was 1. 27%. At the corresponding time of the last 5 individuals, the predicted value was reduced by an average of 1. 42%, and the standard deviation was about 0. 16%. The results show that the surface-temperature prediction-model of missile based on adaptive regression algorithm has high precision to predict the surface temperature of missile, and achieves the purpose of predicting the surface temperature of missile.
KW - Adaptive regression algorithm
KW - Missile
KW - Temperature prediction model
UR - http://www.scopus.com/inward/record.url?scp=85018302650&partnerID=8YFLogxK
M3 - 文章
AN - SCOPUS:85018302650
SN - 1004-499X
VL - 29
SP - 93
EP - 96
JO - Dandao Xuebao/Journal of Ballistics
JF - Dandao Xuebao/Journal of Ballistics
IS - 1
ER -