基于离散GM(1,1)模型的河南省民用车拥有量预测研究
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摘要
在供给侧改革的帮助下,人民的生活水平日益提高.居民拥有民用车的数量不断增加,但过多的民用车辆给交通带来了极大的不便,同时也加重了大气污染.以2011—2016年《河南省统计年鉴》数据为原始数据,通过运用离散GM(1,1)模型,对2017—2019年的河南省民用车拥有量做出合理预测.由于以原始数据进行建模发现误差过大,故引入一阶平均弱化缓冲算子.结果表明:引入平均弱化缓冲算子后的离散GM(1,1)模型误差明显减小,满足精度需求.运用该模型对2017—2019年的河南省民用车拥有量进行合理预测,给出建议.
With the help of supply-side reform,people's living standards are improving day by day.The number of civilian cars owned by residents is increasing,but too many civilian vehicles have brought great inconvenience to traffic and increased atmospheric pollution.Taking the data of Henan Statistical Yearbook from 2011 to 2016 as the original data,a reasonable prediction on the ownership of civil vehicles in Henan province from 2017 to 2019 was maked by using the discrete GM(1,1) model.The first-order average weakening buffer operator is introduced because the error is too large when the original data was modeled.The results showed that the error of discrete GM(1,1)model after introducing average weakening buffer operator is obviously reduced,which meets the accuracy requirements.So the model is used to predict the civil car ownership in Henan province from 2017 to 2019.Finally,some relevant suggestions are given.
With the help of supply-side reform,people's living standards are improving day by day.The number of civilian cars owned by residents is increasing,but too many civilian vehicles have brought great inconvenience to traffic and increased atmospheric pollution.Taking the data of Henan Statistical Yearbook from 2011 to 2016 as the original data,a reasonable prediction on the ownership of civil vehicles in Henan province from 2017 to 2019 was maked by using the discrete GM(1,1) model.The first-order average weakening buffer operator is introduced because the error is too large when the original data was modeled.The results showed that the error of discrete GM(1,1)model after introducing average weakening buffer operator is obviously reduced,which meets the accuracy requirements.So the model is used to predict the civil car ownership in Henan province from 2017 to 2019.Finally,some relevant suggestions are given.
引文
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[10]武孟飞,李炳军.基于GM(1,1)灰色预测模型的物流产业发展研究:以河南省经济增长背景为例[J].河南科学,2018,36(8):1305-1312.
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[14]匡鹏,李刚,刘本希.基于缓冲算子和GM(1,1)模型的电价预测方法[J].水力发电,2018,44(11):109-112,124.
[15]杨飞雪,李晓梅.锦州港油品物流的灰色模型预测分析[J].辽宁工业大学学报(社会科学版),2018,20(5):36-38.
[2]杨帆,吴业军.基于GM(1,1)模型的某高校招生人数预测[J].长春大学学报,2008,18(2):29-30.
[3]代伟,曲东.基于GM(1,1)模型的秦皇岛市大气污染物SO2预测[J].中国环境管理干部学院学报,2011,21(1):55-57,91.
[4]郑国庆,曲红焱.新陈代谢GM(1,1)模型在林业产值预测中的应用[J].黑龙江八一农垦大学学报,2012,24(2):27-29.
[5]王正新,党耀国,刘思峰.基于离散指数函数优化的GM(1,1)模型[J].系统工程理论与实践,2008(2):61-67.
[6]姚天祥,刘思峰.离散GM(1,1)模型的特性与优化[J].系统工程理论与实践,2009,29(3):142-148.
[7]姚天祥,刘思峰,谢乃明.新信息离散GM(1,1)模型及其特性研究[J].系统工程学报,2010,25(2):164-170.
[8]谢乃明,刘思峰.离散GM(1,1)模型与灰色预测模型建模机理[J].系统工程理论与实践,2005(1):93-99.
[9]吴利丰,刘思峰,姚立根.基于分数阶累加的离散灰色模型[J].系统工程理论与实践,2014,34(7):1822-1827.
[10]武孟飞,李炳军.基于GM(1,1)灰色预测模型的物流产业发展研究:以河南省经济增长背景为例[J].河南科学,2018,36(8):1305-1312.
[11]刘思峰.冲击扰动系统预测陷阱与缓冲算子[J].华中理工大学学报,1997(1):26-28.
[12]党耀国,刘思峰,刘斌,等.关于弱化缓冲算子的研究[J].中国管理科学,2004(2):109-112.
[13]崔杰,党耀国,刘思峰.基于新弱化算子的GM(1,1)建模精度分析[J].系统工程理论与实践,2009,29(7):132-138.
[14]匡鹏,李刚,刘本希.基于缓冲算子和GM(1,1)模型的电价预测方法[J].水力发电,2018,44(11):109-112,124.
[15]杨飞雪,李晓梅.锦州港油品物流的灰色模型预测分析[J].辽宁工业大学学报(社会科学版),2018,20(5):36-38.