BP神经网络在铁路建设风险评估中的应用
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摘要
研究目的:中国铁路建设项目是实施"一带一路"战略的重要组成部分,而海外铁路修建的风险评估是项目的首要环节。本文首先综合各项风险因素,主要分析铁路建设项目沿途的亚洲及欧洲部分国家,建立了铁路建设项目风险评价体系;其次,针对亚欧两个大洲政治经济文化上的差异,使用了不同的训练算法建立了两个独立的BP神经网络模型。研究结论:(1)本文针对亚洲和欧洲不同的情况,利用不同的函数建立了BP神经网络模型来进行风险评价;(2)通过所创建的神经网络模型,在铁路建设目标国宏观风险评价中,只需专家给出目标国各个风险的评分,就可以得出目标国的总体建设风险评分,无需再进行繁琐的人工总体评分;(3)本研究结果可用于高速铁路建设风险评估。
Research purposes: Chinese railway construction project is an important part of the implementation of the " Belt and Road" strategy,and the risk evaluation of overseas railway construction is the primary link of the project.Firstly,this paper synthesizes various risk factors,mainly analyzes the Asian and European countries along the railway construction project,and establishes a railway construction project risk evaluation system. Secondly,it uses different training algorithms for the political,economic and cultural differences between the two continents,to establish two independent BP neural network models.Research conclusions:(1) This paper establishes a BP neural network model for risk assessment using different functions for different situations in Asia and Europe.(2) Through the created neural network model,in the macro-risk evaluation of the railway construction target country,only the experts can give the scores of the respective risks in the target country,and the overall construction risk score of the target country can be obtained without tedious manual overall scoring.(3) The research results can be used for risk assessment of high-speed railway construction.
Research purposes: Chinese railway construction project is an important part of the implementation of the " Belt and Road" strategy,and the risk evaluation of overseas railway construction is the primary link of the project.Firstly,this paper synthesizes various risk factors,mainly analyzes the Asian and European countries along the railway construction project,and establishes a railway construction project risk evaluation system. Secondly,it uses different training algorithms for the political,economic and cultural differences between the two continents,to establish two independent BP neural network models.Research conclusions:(1) This paper establishes a BP neural network model for risk assessment using different functions for different situations in Asia and Europe.(2) Through the created neural network model,in the macro-risk evaluation of the railway construction target country,only the experts can give the scores of the respective risks in the target country,and the overall construction risk score of the target country can be obtained without tedious manual overall scoring.(3) The research results can be used for risk assessment of high-speed railway construction.
引文
[1]王作功,李慧洋,贾元华.一种可用于高速公路投资风险评估的神经网络模型[J].交通运输系统工程与信息,2013(4):94-99.Wang Zuogong,Li Huiyang,Jia Yuanhua. A Neural Network Model for Expressway Investment Risk Evaluation and Its Application[J]. Journal of Transportation Systems Engineering and Information Technology,2013(4):94-99.
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[7]王小川,史峰,郁磊,等. MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013.Wang Xiaochuan,Shi Feng,Yu Lei,etc. 43 Case Studies of MATLAB Neural Network[M]. Beijing:Beijing University of Aeronautics and Astronautics Press,2013.
[8] Saidi A,Mirzaei M. Application of Gold-labeled Antibody Biosensor in Simultaneous Determination of Total Aflatoxins Using Artificial Neural Network[J].Journal of the Iranian Chemical Society,2014(2):391-398.
[9] Gaiyun He,Can Huang,Longzhen Guo,Guangming Sun,Dawei Zhang. Identification and Adjustment of Guide Rail Geometric Errors Based on BP Neural Network[J]. Measurement Science Review,2017(3):135-144.
[10] JoséG B,Alfredo H J,Joel A M,etc. Estimation of Umbilical Cord Blood Leptin and Insulin Based on Anthropometric Data by Means of Artificial Neural Network Approach:Identifying Key Maternal and Neonatal Factors[J]. Bmc Pregnancy&Childbirth,2016(1):179-179.
[2]陈明. MATLAB神经网络原理与实例精解[M].北京:清华大学出版社,2013.Chen Ming. Principles and Examples of MATLAB Neural Network[M]. Beijing:Tsinghua University Press,2013.
[3]杨甲沛.基于自适应学习速率的改进型BP算法研究[D].天津:天津大学,2008.Yang Jiapei. Research on Improved BP Algorithm Based on Adaptive Learning Rate[D]. Tianjin:Tianjin University,2008.
[4]安然.基于BP神经网络的物流园区建设风险评价研究[D].西安:长安大学,2013.An Ran. Research on Risk Evaluation of Logistics Park Construction Based on BP Neural Network[D]. Xi'an:Chang'an University,2013.
[5]于作明,王小文.基于BP神经网络的商场建筑火灾风险评价[J].沈阳航空工业学院学报,2007(1):72-74.Yu Zuoming, WangXiaowen. Risk Assessment of Shopping Building Fire Based on BP Neural Network[J]. Journal of Shenyang Institute of Aeronautical Engineering,2007(1):72-74.
[6]崔卫芳,霍学喜,庄世宏,等.基于BP神经网络的农业高科技投资项目风险评价模型[J].西北农林科技大学学报:自然科学版,2006(7):160-164.Cui Weifang,Huo Xuexi,Zhuang Shihong,etc. Risk Evaluation Model of Agricultural High-tech Investment Project Based on BP Neural Network[J].Journal of Northwest Sci-Tech University of Agriculture and Forestry:Natural Science Edition,2006(7):160-164.
[7]王小川,史峰,郁磊,等. MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013.Wang Xiaochuan,Shi Feng,Yu Lei,etc. 43 Case Studies of MATLAB Neural Network[M]. Beijing:Beijing University of Aeronautics and Astronautics Press,2013.
[8] Saidi A,Mirzaei M. Application of Gold-labeled Antibody Biosensor in Simultaneous Determination of Total Aflatoxins Using Artificial Neural Network[J].Journal of the Iranian Chemical Society,2014(2):391-398.
[9] Gaiyun He,Can Huang,Longzhen Guo,Guangming Sun,Dawei Zhang. Identification and Adjustment of Guide Rail Geometric Errors Based on BP Neural Network[J]. Measurement Science Review,2017(3):135-144.
[10] JoséG B,Alfredo H J,Joel A M,etc. Estimation of Umbilical Cord Blood Leptin and Insulin Based on Anthropometric Data by Means of Artificial Neural Network Approach:Identifying Key Maternal and Neonatal Factors[J]. Bmc Pregnancy&Childbirth,2016(1):179-179.