基于NARX神经网络的城市汽车保有量区间估计及灵敏度分析

分析了影响汽车保有量的因素,运用灰色关联度理论选取主要影响因子,并采用主成分分析法对选定因子进行了相关性处理和降维处理,针对选取的相关因子建立了NARX神经网络预测模型。以此为基础,根据长沙市2000-2012年各指标的历史数据,对该市2013～2020年汽车保有量进行了区间预测,并进行了误差分析和灵敏度分析。研究结果表明,2013～2020年间该市汽车保有量的增加速度较为稳定,到2020年该市汽车保有量总数达1902847辆,修正后的预测值所属区间为E1891715,1913979];当经济增长速度降低1％时,汽车保有量平均增长速度降低0．53％;且政策对该市汽车保有量具有显著性影响。

Car ownership interval estimation and sensitivity analysis for city based on NARX neural network

The influence factors of car ownership are analysed and the main factors are selected by applying grey correlation analysis.The relevances among the selected indexes are removed by using principal component analysis, and the number of indexes has reduced simultaneously.According to the selected indexes, a dynamic neural network for NARX is established to predict the interval of car ownership. Taking the historical data of 2000-2012 in Changsha as input data,the car ownership interval of 2013-2020 is estimated and the corresponding error analysis and sensitivity analysis are developed.The results show the increasing speed of car ownership for 2013-2020 is steady and the estimated value for Chang- sha belongs toil 891 715, 1 913 979] in 2020, and when the GDP＇s growth rate reduces per one percent,the car ownership growth rate reduces 0.53 percent, and the policy has a significant influence on the city car ownership.