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基于数据适应性检验的身管磨损量预测研究
引用本文:王国辉,赵硕,李向荣,范鹏飞.基于数据适应性检验的身管磨损量预测研究[J].火力与指挥控制,2017,42(3).
作者姓名:王国辉  赵硕  李向荣  范鹏飞
作者单位:装甲兵工程学院,北京,100072
摘    要:应用灰色系统理论研究不确定系统,对系统进行灰色预测模型建立的同时,需要检验系统原始数据序列是否符合建模要求。主要从3个方面检验数据建模的可行性,分别是光滑比、极比偏差和凹凸性检验。结合某型坦克炮身管内膛烧蚀磨损数据,采用不同的磨损数据作为灰色预测模型建模的原始数据序列,具体分成两种情况进行数据检验,并代入模型计算,分析不同情况下模型的预测精度,通过对比计算结果选出与模型匹配度更高的原始数据序列。

关 键 词:光滑比  极比偏差  凹凸性  灰色预测模型  身管磨损量

Research on Body Tube Wear Prediction Based on Data Adaptability Test
WANG Guo-hui,ZHAO Shuo,LI Xiang-rong,FAN Peng-fei.Research on Body Tube Wear Prediction Based on Data Adaptability Test[J].Fire Control & Command Control,2017,42(3).
Authors:WANG Guo-hui  ZHAO Shuo  LI Xiang-rong  FAN Peng-fei
Abstract:Application of grey system theory to study uncertain system,to carry on the grey prediction model of the system,and need to check whether conform to the requirements of the modeling system original data sequence. Mainly from three aspects test the feasibility of data modeling,smooth ratio,class ratio dispersion and convex-concave test. Combined with a certain type of tank guns the ablation wear bore data,using different wear data as modeling the original data sequence grey prediction model,and the specific analysis of test data is divided into two kinds of circumstances,and generation into the model calculation,by comparing the calculation results to select with a higher degree of model matching the original data sequence.
Keywords:smooth ratio  class ratio dispersion  convex-concave  the grey prediction model  body tube wear
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