The application of measured shortterm data to the prediction of longterm stability of weapon system is significant to shorten the production cycle of weapons. Considering such prediction problems as inadequate data and small sample sequence, optimized algorithm model was presented based on the drawback analysis of GM(1,1) prediction model. The optimized prediction methods were generalized as multiparameter prediction. At first, the model which used the latest measured data for initialization was established, followed by replacing the old information with the latest through metabolic approaches to realize equal dimension model predication. In addition, fading memory recursive least squares method was adopted for weighted handling of old and new information. The normalized mean relative error was used as accuracy test standard for background value and particle swarm optimization algorithm was adopted. Finally, the calibration parameters stability of a certain type of inertial measurement unit (IMU) was predicted, and the average relative error of the prediction results was reduced by 6%~58%. The results indicate that the prediction method can be applied to the longterm stability of IMU calibration parameters.
ZHANG Zhaofei，LUO Jianjun，XU Binghua，MA Weihua
. Metabolism Adaptive MultiParameter Prediction
Method Based on Grey Theory[J]. Journal of Shanghai Jiaotong University, 2017
, 51(8)
: 970
-976
.
DOI: 10.16183/j.cnki.jsjtu.2017.08.011
［1］HEIJDEN M V D, VELIKOVA M, LUCAS P J F. Learning Bayesian networks for clinical time series analysis［J］. Journal of Biomedical Informatics, 2013, 48(2): 94105.
［2］TIAN W D, HU M G, LI C K. Fault prediction based on dynamic model and grey time series model in chemical processes［J］. Chinese Journal of Chemical Engineering, 2014, 22(6): 643650.
［3］谭鹏, 曹平. 基于灰色关联支持向量机的地表沉降预测［J］. 中南大学学报(自然科学版), 2012, 43(2): 632637.
TAN Peng, CAO Ping. Predicting surface settlement of tunnel using greyrelationalsupport vector machine［J］. Journal of Central South University (Science and Technology), 2012, 43(2): 632637.
［4］李松, 刘力军, 翟曼. 改进粒子群算法优化BP神经网络的短时交通流预测［J］. 系统工程理论与实践, 2012 (9): 20452049.
LI Song, LIU Lijun, ZHAI Man. Prediction for short term traffic flow based on modified PSO optimized BP neural network［J］. Systems EngineeringTheory & Practice, 2012 (9): 20452049.
［5］朱晓菲, 王国华, 张欣豫, 等. 基于遗传新陈代谢灰色模型的电子设备故障预测模型［J］. 现代电子技术, 2014, 37(1): 8689.
ZHU Xiaofei, WANG Guohua, ZHANG Xinyu, et al. Research on failure prediction of electronic equipment based on genetic metabolism grey model［J］. Modern Electronics Technique, 2014, 37(1): 8689.
［6］罗党, 刘思峰, 党耀国. 灰色模型GM(1,1)优化［J］. 中国工程科学, 2003, 5(8): 5053.
LUO Dang, LIU Sifeng, DANG Yaoguo. The optimization of grey model GM(1,1)［J］. Engineering Science, 2003, 5(8): 5053.
［7］郭阳明, 姜红梅, 翟正军. 基于灰色理论的自适应多参数预测模型［J］. 航空学报, 2009, 30(5): 925931.
GUO Yangming, JIANG Hongmei, ZHAI Zhengjun. Adaptive multiparameter prediction model based on grey theory［J］. Acta Aeronautica et Astronautica Sinica, 2009, 30(5): 925931.
［8］李丁, 夏露. 改进的粒子群优化算法在气动设计中的应用［J］. 航空学报, 2012, 10(10): 18091816.
LI Ding, XIA Lu. Application of improved particle swarm optimization algorithm to aerodynamic design［J］. Acta Aeronautica et Astronautica Sinica, 2012, 10(10): 18091816.
［9］许榕,周东,蒋士正,等. 自适应粒子群神经网络交通流预测模型［J］. 西安交通大学学报, 2015 (10): 103108.
XU Rong, ZHOU Dong, JIANG Shizheng, et al. A traffic forecasting model using adaptive particle swarm optimization trained neural network［J］. Journal of Xi’an Jiaotong University, 2015 (10): 103108.
［10］党建军, 罗建军, 万彦辉. 基于单轴速率转台的捷联惯测组合标定方法［J］. 航空学报, 2010, 31(4): 806811.
