计算机工程与应用 ›› 2019, Vol. 55 ›› Issue (16): 246-254.DOI: 10.3778/j.issn.1002-8331.1812-0156

• 工程与应用 • 上一篇    下一篇

城市物流竞争力分析DAE-WMA优化算法

李楠,侯旋   

  1. 1.中国(西安)丝绸之路研究院,西安 710100
    2.西安财经学院 管理学院,西安 710100
    3.空军工程大学 航空工程学院,西安 710038
  • 出版日期:2019-08-15 发布日期:2019-08-13

Urban Logistics Competitiveness Analysis on DAE-WMA Optimization Algorithm

LI Nan, HOU Xuan   

  1. 1.China (Xi’an) Institute for Silk Road Research, Xi’an 710100, China
    2.School of Management, Xi’an University of Finance and Economics, Xi’an 710100, China
    3.College of Aeronautics Engineering, Air Force Engineering University, Xi’an 710038, China
  • Online:2019-08-15 Published:2019-08-13

摘要: 深入分析了城市物流竞争力的研究现状,结合深度学习相关理论,以深层自编码器(Deep Auto Encoder,DAE)标准模型与标准算法为基础,提出了基于Widrow函数的深层自编码器动量更新算法(DAE-WMA)。依据城市物流竞争力分析数据量特点,选取三种UCI数据集,对基于误差函数的标准算法(DAE-ESA)、基于交叉熵的标准算法(DAE-CSA)以及DAE-WMA的模式分类能力进行仿真,仿真结果表明后者的性能优于前两者。依据物流竞争实力与竞争潜力,基于层次分析法(Analytic Hierarchy Process,AHP)通过选取7个评估维度与19个评价指标构建城市物流竞争力指标体系,利用DAE-WMA方法与社会网络分析(Social Network Analysis,SNA)方法,对我国西北五省区13个主要城市的物流竞争力进行聚类分析与实证研究,仿真结果表明DAE-WMA方法相对于SNA方法,对核心节点城市的分类结果更加合理,更有利于对问题的分析。研究结果为确定新丝绸之路经济带沿线城市物流发展策略,促进国内物流业未来的协作与发展奠定了研究基础。

关键词: 物流竞争力, 社会网络分析, 深度学习, 深层自编码器, 动量

Abstract: This paper analyzes the situation of urban logistics competitiveness deeply, constructs a Deep Auto Encoder Momentum Update Algorithm based on Widrow Function(DAE-WMA), combined with the relevant theory of deep learning and the standard model and standard algorithm of Deep Auto Encoder(DAE). According to the data volume characteristics of urban logistics competitiveness, this paper selects three types of UCI datasets to simulate the Standard Algorithm based on Error Function(DAE-ESA), the Standard Algorithm based on Cross Entropy Function(DAE-CSA), as well as the recognition ability of DAE-WMA. The simulation results show that the performance of the latter is better than the first two. Secondly, according to the competitive strength and competitive potential of logistics, based on Analytic Hierarchy Process(AHP), the paper constructs the index system of city logistics competitiveness with 7 dimensions and 19 indicators, carries on cluster analysis and empirical research on logistics competitiveness of 13 core cities in our northwest country, using DAE-WMA and Social Network Analysis(SNA). Simulation results show that DEA-WMA is more effective than SNA, the classification results of the core node cities are more reasonable and more conducive to analyzing problems. The research results lay the foundation for determining the developing strategy on logistics of cities along the New Silk Road and promoting the future cooperation and development of the domestic logistics industry.

Key words: logistics competitiveness, social network analysis, deep learning, Deep Auto Encoder(DAE), momentum