Prediction of Gun Barrel Wear Based on Improved Non-equal Interval Grey Model and BP Neural Network

doi:10.3969/j.issn.1000-1093.2016.12.006

Abstract

Abstract: General data fitting and prediction methods are constrained by the unequal time interval, difficult sampling, high cost and small amount of data in predicting of the wear degrees of gun barrel. A combined prediction method based on the improved unequal interval grey model and neural network is proposed. The proposed method is used to predict the wear of gun tube. The predicted results agree well with the experimental values. The results show that the combined prediction method has high prediction accuracy, and therefore can be used to effectively predict the gun bore wear.

Key words: ordnance science and technology, gun barrel wear, unequal Interval, grey model, BP neural network, combined prediction method

CLC Number:

TJ301

YI Huai-jun, LIU Ning, ZHANG Xiang-yan, DING Chuan-jun. Prediction of Gun Barrel Wear Based on Improved Non-equal Interval Grey Model and BP Neural Network[J]. Acta Armamentarii, 2016, 37(12): 2220-2225.

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