Metabolism Adaptive MultiParameter Prediction
 Method Based on Grey Theory

doi:10.16183/j.cnki.jsjtu.2017.08.011

Abstract

Abstract: The application of measured shortterm data to the prediction of longterm 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 multiparameter 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 longterm stability of IMU calibration parameters.

Key words: grey theory; metabolism; adaptive; prediction; particle swarm optimization

CLC Number:

V 241.6

ZHANG Zhaofei，LUO Jianjun，XU Binghua，MA Weihua. Metabolism Adaptive MultiParameter Prediction
Method Based on Grey Theory[J]. Journal of Shanghai Jiao Tong University, 2017, 51(8): 970-976.

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