Simpson改进的灰色神经网络在汽车保有量中的预测

doi:10.3969/j.issn.1674-0696.2019.09.17

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (556 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要: 针对汽车保有量数据具有非线性和随机性的特点，建立基于Simpson公式的灰色神经网络模型对汽车保有量进行预测研究；利用Simpson公式对经典GM(1,1)灰色系统的背景值进行改进以提高模型的预测精度；通过相关性分析，确定国民总收入、人均国内生产总值、总人口、固定资产投资、进出口总额、钢材产量、社会消费品零售总额7个因素为汽车保有量的影响因素，并将7个影响因素作为BP神经网络的输入建立BP神经网路模型；根据灰色系统和BP神经网络预测误差大小确定组合模型的权重，构建灰色神经网络组合模型；对比分析经典GM(1,1)、Simpson公式的GM(1,1)、BP神经网络、灰色神经网络、Simpson公式的灰色神经网络模型的计算结果。研究表明：基于Simpson公式的灰色神经网络预测精度最高，其相对误差均在3%以内，相对误差的方差为3.2780，小于灰色神经网络模型和单一预测模型。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	吴文青1
	夏杰2

关键词 ：车辆工程, 汽车保有量, 背景值, Simpson公式的GM(1, 1)模型, 组合预测模型, 预测精度

Abstract：According to the features of non-linearity and randomness of car ownership data, a grey neural network model with Simpson formula was established to predict Chinas car ownership. First, the formula was employed to construct the background value of classic GM (1,1) for improving prediction accuracy; second, such seven factors of car ownership as total gross national income, per capita gross domestic product, total population, investment in fixed assets, total import and export, steel output and total retail sales of consumer goods were viewed by correlation analysis and then the input of neural network was used for building BP neural network model. To determine the weight of the combined model with the prediction error of grey system and BP neural network, the grey neural network model was constructed. With the comparison among the classic grey GM (1,1), GM (1,1) of Simpson formula, BP neural network, grey neural network, Simpson formula grey neural network model, the results show that grey neural network with Simpson formula has the highest prediction accuracy where the relative error is as low as 3% and the mean square deviation is 3.2780%, which is smaller than grey neural network model and single prediction model.

Key words ： vehicle engineering, car ownership, background value, GM (1,1) model of Simpson formula, combined forecasting model, prediction accuracy

收稿日期: 2018-01-12

PACS:

U491.4

基金资助:教育部人文社科青年基金项目(19YJCZH119)；西南科技大学博士研究基金项目(15zx7141)

作者简介: 吴文青(1986—)，男，四川达州人，博士，主要从事灰色理论及其应用、复杂系统建模方面的研究。E-mail: wwqing0704@163.com。通信作者：夏杰(1996—)，男，云南宣威人，硕士，主要从事灰色理论及其应用方面的研究。E-mail: swust20171001@163.com。

引用本文:

吴文青1，夏杰2. Simpson改进的灰色神经网络在汽车保有量中的预测[J]. 重庆交通大学学报（自然科学版）, 2019, 38(09): 101-108.
WU Wenqing1, XIA Jie2. Prediction of Chinas Car Ownership by Grey Neural Network with Simpson Formula. Journal of Chongqing Jiaotong University(Natural Science), 2019, 38(09): 101-108.

链接本文:

http://xbzk.cqjtu.edu.cn/CN/10.3969/j.issn.1674-0696.2019.09.17 或 http://xbzk.cqjtu.edu.cn/CN/Y2019/V38/I09/101

［1］胡军红,李晶.各种运输方式协调发展模式探讨［J］.重庆交通大学学报(自然科学版),2009,28(2):294-297.
HU Junhong, LI Jing. Discussion on patterns of transport modes coordinated development ［J］.Journal of Chongqing Jiaotong University (Natural Science), 2009,
28(2):294-297.
［2］ JONG G D, FOX J, DALY A, et al. Comparison of car ownership models［J］. Transport Reviews, 2004, 24(4):379-408.
［3］ CLARK B, CHATTERJEE K, MELIA S. Changes in level of house-hold car ownership: the role of life events and spatial context［J］ Transportation, 2016,43(4)：
565-599.
［4］周骞, 杨东援. 基于多相关因素的汽车保有量预测神经网络方法［J］. 公路交通科技, 2001, 18(6):126-129.
ZHOU Qian, YANG Dongyuan. Multi-relative factor forecast of vehicle population by neural network ［J］.Journal of Highway and Transportation Research and
Development, 2001, 18(6):126-129.
［5］夏钰, 陈学武. 基于神经网络BP算法的出租汽车保有量预测法［J］. 交通信息与安全, 2005, 23(5):35-37.
XIA Yu, CHEN Xuewu. Prediction of rental car ownership based on neural network BP algorithm ［J］.Journal of Transport Information and Safety, 2005, 23 (5): 35-37.
［6］王琦, 王花兰. 基于熵值法的城市汽车保有量组合预测［J］. 交通科技与经济, 2009, 11(6):53-55.
WANG Qi,WANG Hualan. Combination forecasting of vehicle population based on the entropy method ［J］.Technology & Economy in Areas of Communications, 2009, 11
(6):53-55.
［7］孙璐, 郁烨, 顾文钧. 基于PCA和HMM的汽车保有量预测方法［J］. 交通运输工程学报, 2013, 13(2):92-98.
SUN Lu, YU Ye,GU Wenjun. Car ownership prediction method based on Principal Component Analysis and Hidden Markov Model［J］. Journal of Traffic and Transportation
Engineering, 2013, 13(2):92-98.
［8］王栋. 基于灰色关联和BP神经网络的汽车保有量预测［J］. 计算技术与自动化, 2015(1):29-33.
WANG Dong. Prediction of car ownership based on gray relational analysis and BP neural network［J］.Computing Technology and Automation,2015(1):29-33.
［9］王明锐, 雷君, 郭望华. 基于计量经济学模型的武汉市汽车保有量预测［J］. 汽车科技, 2017(4):45-48.
WANG Mingrui, LEI Jun, GUO Wanghua. Forecast of vehicle population in Wuhan based on model of econometrics ［J］.Auto Sci-tech, 2017(4):45-48.
［10］张兰怡, 胡喜生, 陈清耀,等. 基于PCA-Logistic回归的汽车保有量预测研究［J］. 重庆交通大学学报(自然科学版), 2017,5(5):108-113.
ZHANG Lanyi,HU Xisheng,CHEN Qingyao,et al. Prediction of car ownership based on principal component analysis and logistic regression ［J］. Journal of Chongqing
Jiaotong University (Natural Science), 2017,5(5):108-113.
［11］罗志军, 黄立新, 雷霆,等. 基于粒子群算法的汽车保有量预测方法［J］. 计算机测量与控制, 2017,9(9):146-149.
LUO Zhijun,HUANG Lixin, LEI Ting et al, Car ownership prediction based on PSO ［J］.Computer Measurement & Control, 2017,9(9):146-149.
［12］ DENG J L. Control problems of grey systems［J］. Systems & Control Letters, 1982, 1(5):288-294.
［13］李剑波,鲜学福.基于灰色神经网络模型的重庆能源需求预测［J］.西南大学学报(自然科学版),2016,38(6):136-141.
LI Jianbo,XIAN Xuefu. Energy demand forecasting of Chongqing based on gray neuralnetwork model ［J］.Journal of Southwest University (Natural Science Edition),
2016,38(6):136-141.
［14］李柏年,吴礼斌.MATLAB数据分析方法［M］.北京:机械工业出版社,2012:170-172.
LI Bainian, WU Libin.MATLAB Data Analysis Method ［M］.Beijing: Mechanical Industry Press, 2012: 170-172.
［15］唐小我. 组合预测计算方法研究［J］.预测,1991(4):35-39.
TANG Xiaowo.Research on combinatorial prediction calculation method［J］.Forecasting, 1991(4):35-39.
［16］周素霞, 王明智, 夏训峰, 等.最优组合预测模型在城市生活垃圾清运量中的应用［J］.环境科学与技术, 2010, 33(9):204-207.
ZHOU Suxia,WANG Mingzhi,XIA Xunfeng, et al. Application of optimal combination forecast model in forecasting delivering quantity of MSW in China［J］.Environmental
Science & Technology, 2010, 33(9):204-207.
［17］亓芳芳.基于面板数据模型的中国民用汽车消费需求预测研究［D］.合肥:中国科学技术大学, 2010.
QI Fangfang. Forecasting Research on the Consumption Demand of Civilian Vehicle in China based on Panel Data Model［D］. Hefei:University of Science and Technology
of China,2010.
［18］中华人民共和国国家统计局.中国统计年鉴2017［M］.北京:中国统计出版社,2017.
National Bureau of Statistics of China. China Statistical Yearbook 2017 ［M］. Beijing: China Statistics Press, 2017.