基于GA-BP模型的城市电子商务竞争力评价——以长三角国家电商示范城市为例
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摘要
借鉴前人研究的基础,建立21个指标的城市电子商务竞争力评价指标体系,选取长三角国家电子商务示范城市,运用遗传算法优化神经网络对上述城市电子商务竞争力进行评价和分析。结果表明,(1)本地化园区水平、电商衍生服务水平、网商密度水平、快递服务水平和电子商务相关政策是影响城市电子商务竞争力的关键指标。(2)长三角10个国家电子商务示范城市电子商务竞争力虽然在中国居于较高水平,但发展不平衡。(3)构建城市电子商务竞争力评价GA-BP模型具有较好的科学性和可操作性,根据评价值判断城市电子商务竞争态势,比照指标体系寻找差距加以完善,将是城市电子商务竞争力研究的发展方向。
Based on the predecessor research,we established 21 indicators of urban e-commerce competitiveness evaluation index system,and use the GA-BP algorithm to analyze ten national e-commerce demonstration cities of Yangtze river delta. The results showed that: Local e-commerce park,derivative service,the density of electronic retailing,express delivery service and related policy are the key indicators of urban e-commerce competitiveness. These 10 cities' e-commerce competitiveness in the Yangtze river delta are at a higher level in China but unbalanced in development,and is divided into three categories according to the evaluation level and competition situation. Judging the competitiveness of urban e-commerce by GA-BP model,comparing it with the index system to find the gaps will be a research direction of urban e-commerce competitiveness.
Based on the predecessor research,we established 21 indicators of urban e-commerce competitiveness evaluation index system,and use the GA-BP algorithm to analyze ten national e-commerce demonstration cities of Yangtze river delta. The results showed that: Local e-commerce park,derivative service,the density of electronic retailing,express delivery service and related policy are the key indicators of urban e-commerce competitiveness. These 10 cities' e-commerce competitiveness in the Yangtze river delta are at a higher level in China but unbalanced in development,and is divided into three categories according to the evaluation level and competition situation. Judging the competitiveness of urban e-commerce by GA-BP model,comparing it with the index system to find the gaps will be a research direction of urban e-commerce competitiveness.
引文
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[2]郑亚琴.我国农村电子商务区域基础设施发展水平的主成分聚类分析[J].中国科技论坛,2007(1):119-122.
[3]刘跃,王文庆.区域电子商务发展指数的重构及实证分析[J].科学学与科学技术管理,2009,30(7):144-147.
[4]张野,明茹雪,杨帆.电子商务发展指数的测算模型构建方法[J].电子设计工程,2012,20(22):149-150.
[5]汤英汉.中国电子商务发展水平及空间分异[J].经济地理,2015,35(5):9-14.
[6]穆燕鸿,王杜春.黑龙江省农村电子商务发展水平测度实证分析:以15个农村电子商务综合示范县为例[J].江苏农业科学,2016,45(5):608-611.
[7]LIU L H,CHEN J,XU L X. Realization and application research of BP neural network based on MATLAB[C]//2008 International Seminar on Future Biomedical Information Engineering. Wuhan:Institute of Electrical and Electronics Engineers,2008:130-133.
[8]李虎. BP算法的改进及其在PID优化控制中的应用研究[D].西安:西安科技大学,2012.
[9]LU Y,LEVY D,ZHANG J. An evolutionary performance-enhanced PSO approach by using a BP neural-learning-based PID controller[J]. Connection Science,2011,23(1):45-63.
[10]XIAO Z,YE S J,ZHONG B. BP neural network with rough set for short term load forecasting[J]. Expert Systems with Applications,2009,36(1):273-279.
[11]李国正,张波.基于Duffing振子检测频率未知微弱信号的新方法[J].仪器仪表学报,2017,38(01):181-189.
[12]LEAMER E. Regression selection strategies and revealed priors[J].Publications of the American Statistical Association,1978,73(363):580-587.
[13]FERNANDEZ C,LEY E,STEEL M. Benchmark priors for Bayesian model averaging[J]. Econometrics,1998,100(2),381-427.
[14]黄松,李燕林,戴平娟.智慧旅游城市旅游竞争力评价[J].地理学报,2017,72(2):241-254.