| 基于优化的非等间隔灰色理论和BP神经网络的身管磨损量预测 |
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| 引用本文： | 易怀军,刘宁,张相炎,丁传俊.基于优化的非等间隔灰色理论和BP神经网络的身管磨损量预测[J].兵工学报,2016,37(12):2220-2225. |
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| 作者姓名： | 易怀军 刘宁 张相炎 丁传俊 |
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| 作者单位： | （南京理工大学 机械工程学院， 江苏 南京 210094） |
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| 基金项目： | 江苏省自然科学基金项目(BK20140789)，中央高校基本科研业务费专项资金项目(30915118826) |
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| 摘 要： | 针对火炮身管烧蚀磨损量预测中存在的数据采样时间间隔不均匀、采样难度大、成本高、数据量小，常规数据拟合和预测方法难以处理等问题，提出一种基于改进的非等间隔灰色理论和BP神经网络的组合预测方法。通过组合预测模型对某型火炮身管的烧蚀磨损量进行预测，实例分析表明该组合预测模型具有较高的预测精度，为身管内膛磨损量的预测提供了一种新的技术途径。
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| 关 键 词： | 兵器科学与技术 身管磨损 非等间隔 灰色模型 BP神经网络 组合预测模型 |
| 收稿时间： | 2015-04-23 |
Prediction of Gun Barrel Wear Based on Improved Non-equal Interval Grey Model and BP Neural Network |
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| 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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| Authors: | YI Huai-jun LIU Ning ZHANG Xiang-yan DING Chuan-jun |
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| Affiliation: | (School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China) |
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| 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. |
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| Keywords: | ordnance science and technology gun barrel wear unequal Interval grey model BP neural network combined prediction method |
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