基于BP神经网络对BT项目风险的评价
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摘要
BT融资模式是加快城镇化发展过程中,大型基础设施项目建设的主要融资方式,准确地对BT工程项目的风险等级进行评价,有助于降低投资人的风险损失。为了更好地评价BT工程项目的风险等级,克服传统风险分析方法的不足,建立BP神经网络模型。本文以A市地铁BT工程项目为例,首先建立该项目的风险评价指标体系,然后确定合适的BP神经网络模型参数。在大量经验数据的基础上,对BP神经网络模型进行了训练与检测,通过试验发现构建18-17-1三层BP神经网络模型对风险等级的评价结果精度最高。最后通过此模型对A市地铁BT工程项目的风险等级进行了评价,取得较好的效果。
BT financing mode is the main financing large infrastructure projects in the rapid development of urbanization. Accurate evaluation of BT project's risk helps to reduce the investor's risk loss. In order to better evaluating the BT project's risk level and overcoming the shortcomings of the traditional risk analysis methods,a BP neural network model has been used in this paper. BT project's risk level of a city's subway as an example in the paper. Firstly,establish the project risk evaluation index system and determine the appropriate parameters of BP neural network. Meanwhile,based on a large amount of empirical data,the BP neural network model is trained and detected. The experiment showed that the evaluation results on the risk which base on the construction of 18-17-1 three layers BP neural model has a high accuracy. Finally,BT project's risk level of a city's subway was evaluated by the model and good results have been received.
BT financing mode is the main financing large infrastructure projects in the rapid development of urbanization. Accurate evaluation of BT project's risk helps to reduce the investor's risk loss. In order to better evaluating the BT project's risk level and overcoming the shortcomings of the traditional risk analysis methods,a BP neural network model has been used in this paper. BT project's risk level of a city's subway as an example in the paper. Firstly,establish the project risk evaluation index system and determine the appropriate parameters of BP neural network. Meanwhile,based on a large amount of empirical data,the BP neural network model is trained and detected. The experiment showed that the evaluation results on the risk which base on the construction of 18-17-1 three layers BP neural model has a high accuracy. Finally,BT project's risk level of a city's subway was evaluated by the model and good results have been received.
引文
[1]蔡文瑛.BT建设项目风险评价研究[D].西安:西安建筑科技大学,2012.
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[3]杨俊辉,程银侠.基于BP神经网络的工程项目风险评价[J].西安邮电学院学报,2011,16(1):115-118.
[4]彭瑞华.武汉市轨道交通8号线施工地质灾害分析[J].山西建筑,2011,37(6):107-109.
[5]何璠.基于BP人工神经网络的环境质量评价模型研究[D].四川:四川大学,2006.
[6]李阳,王祖志.基于BP神经网络的BOT项目风险评价模型研究[J].中国水运,2008,8(3):250-251.
[7]丛爽.面向MATLAB工具箱的神经网络理论与应用[M].合肥:中国科学技术大学出版社,2009.
[8]王爱华,孙俊.BP神经网络在工程项目管理中的应用[J].建筑管理现代化,2009,23(4):306-309.
[9]张泽旭.神经网络控制与MATLAB仿真[M].哈尔滨:哈尔滨工业大学出版社,2011.
[10]肖先波.基于BP神经网络的公路建设项目风险分析[D].浙江:浙江大学,2006.
[2]飞思科技产品研发中心.MATLAB6.5辅助神经网络分析与设计[M].北京:电子工业出版社,2003.
[3]杨俊辉,程银侠.基于BP神经网络的工程项目风险评价[J].西安邮电学院学报,2011,16(1):115-118.
[4]彭瑞华.武汉市轨道交通8号线施工地质灾害分析[J].山西建筑,2011,37(6):107-109.
[5]何璠.基于BP人工神经网络的环境质量评价模型研究[D].四川:四川大学,2006.
[6]李阳,王祖志.基于BP神经网络的BOT项目风险评价模型研究[J].中国水运,2008,8(3):250-251.
[7]丛爽.面向MATLAB工具箱的神经网络理论与应用[M].合肥:中国科学技术大学出版社,2009.
[8]王爱华,孙俊.BP神经网络在工程项目管理中的应用[J].建筑管理现代化,2009,23(4):306-309.
[9]张泽旭.神经网络控制与MATLAB仿真[M].哈尔滨:哈尔滨工业大学出版社,2011.
[10]肖先波.基于BP神经网络的公路建设项目风险分析[D].浙江:浙江大学,2006.