通信学报 ›› 2017, Vol. 38 ›› Issue (12): 153-159.doi: 10.11959/j.issn.1000-436x.2017290

• 学术通信 • 上一篇    下一篇

面向欠发达地区大数据产业发展能力分析的网络化方法研究

沈俊鑫,陈颖谦   

  1. 昆明理工大学管理与经济学院,云南 昆明 650093
  • 修回日期:2017-11-11 出版日期:2017-12-01 发布日期:2018-01-19
  • 作者简介:沈俊鑫(1978-),男,福建漳州人,博士,昆明理工大学教授、硕士生导师,主要研究方向为数据驱动项目决策。|陈颖谦(1993-),女,湖北咸宁人,昆明理工大学硕士生,主要研究方向为数据驱动项目决策。
  • 基金资助:
    国家自然科学基金资助项目(70962003);教育部人文社科研究基金资助项目(14YJC630107);云南省哲学社会科学研究基地基金资助项目(JD2016ZD02);昆明理工大学人文社会科学研究培育基金资助项目(SKPYPY201645)

Research on network analysis method for development ability of big data industry in underdeveloped area

Jun-xin SHEN,Ying-qian CHEN   

  1. School of Management and Economics,Kunming University of Science and Technology,Kunming 650093,China
  • Revised:2017-11-11 Online:2017-12-01 Published:2018-01-19
  • Supported by:
    The National Natural Science Foundation of China(70962003);Humanities Social Sciences Research Project Ministry of Education in China(14YJC630107);Key Project of Philosophy and Social Science Research Base of Yunnan Province in China(JD2016ZD02);Humanities and Social Sciences Research Project Kunming University of Science and Technology(SKPYPY201645)

摘要:

针对传统产业发展能力评价方法依赖决策者的主观判断,缺乏客观性等问题,提出应用BP神经网络模型进行大数据产业发展能力评价,设计基于熵权的BP神经网络评价模型。建立欠发达地区大数据产业发展能力评价指标体系,以贵州省产业发展数据作为实验样本,采用熵权法确定BP网络期望输出,并与BP网络实际输出进行比较。实验结果表明,所提熵权-BP评价模型优化了单一BP神经网络求权重可能带来的较大误差,提高评价准确性与客观性,适用于欠发达地区大数据产业发展能力评价。

关键词: 大数据产业, 发展能力, BP模型, 熵权法

Abstract:

Traditional evaluation methods of industrial development ability were mostly lack of objectivity.An evaluation model was proposed by using a BP neural network based on entropy weight.Evaluation index system of big data industry development ability in underdeveloped areas was established.Taking Guizhou industrial development data as samples,entropy weight method was used to determine expected output and compared with the actual output .The experimental results show that the proposed entropy weight-BP evaluation model can optimize error of using single BP network and improve the accuracy and objectivity of evaluation.

Key words: big data industry, development ability, BP model, entropy method

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