新型自适应RBF神经网络应用于PPP模式风险控制研究
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摘要
伴随着PPP模式在基础设施领域的广泛应用,其潜存的风险也开始备受关注。各利益主体的行为控制在怎样的一种风险边界之内,才能实现预期的收益?为此,本文将通过新型自适应RBF神经网络对各利益主体的行为进行数理拟合测度。在详细剖析PPP项目利益相关者互动关系的基础上,从正反两个方向对主线型的三大核心利益主体行为进行建模分析,进行智能预测,同时通过对自身最低收益的确定找寻风险控制的临界值,进而有针对性的实施风险控制策略,为PPP项目风险智能测度提供理论上的参考。
With the wide application of PPP model in infrastructure, much attention has been paid to the risk of cesarean. Control in what kind of a boundary on all the stakeholders' behavior, to achieve the expected benefits? Therefore, this article will measure the behavior of all the stakeholders through new adaptive RBF neural network. On the basis of detailed analysis of the interaction during the PPP project stakeholders, Analysis of the behavior on the main line of three core stakeholders from positive and negative two directions. The results showed that through adaptive RBF neural network model can realize intelligent prediction of each benefit main body of its own risk, At the same time can also find the critical value of risk control when which be based on the determination of minimum income, and then implementing targeted risk control strategy, which provided theoretical reference for the intelligent measure of PPP project risks.
With the wide application of PPP model in infrastructure, much attention has been paid to the risk of cesarean. Control in what kind of a boundary on all the stakeholders' behavior, to achieve the expected benefits? Therefore, this article will measure the behavior of all the stakeholders through new adaptive RBF neural network. On the basis of detailed analysis of the interaction during the PPP project stakeholders, Analysis of the behavior on the main line of three core stakeholders from positive and negative two directions. The results showed that through adaptive RBF neural network model can realize intelligent prediction of each benefit main body of its own risk, At the same time can also find the critical value of risk control when which be based on the determination of minimum income, and then implementing targeted risk control strategy, which provided theoretical reference for the intelligent measure of PPP project risks.
引文
[1]Lyons T,Skitmore M.Project Risk Management in the Queensland Engineering Construction Industry:A Survey[J].International Journal of Project Management,2004,22(01):51.
[2]Ebrahimnejad,S.,Mousavi,S.M.Seyrafianpour,H..Risk Identification and Assessment for Build-operate-transfer Projects:A Fuzzy Multi Attribute Decision Making Model[J].Expert Systems with Applications,2010,(37):575-586.
[3]李妍,赵蕾.新型城镇化背景下的PPP项目风险评价体系的构建——以上海莘庄CCHP项目为例[J].经济体制改革,2015,(05):17-23.
[4]Woodward D G.Use of Sensitivity Analysis in Build-ownoperate-transfer Project Evaluation[J].International Journal of Project Management,1995,13(04):239-246.
[5]张玮,张卫东.基于网络层次分析法(ANP)的PPP项目风险评价研究项目[J].项目管理技术,2012,(10):84-88.
[6]何健.基于多层次动态模糊评判的水电项目融资风险评价模型[J].广西水利水电,2008,(02):29-32.
[7]蔡晓琰,周国光.交通基础设施PPP项目智能风险评价模型研究[J].技术经济与管理研究,2016,(08):20-24.
[8]汪学清,黄嘉林,胡德俊.基于PSO-RBF神经网络的项目招标评标应用研究[J].工程管理学报,2014,(06):96-101.
[9]廖力臻,王宏辉.基于PCA-BP-GA的BOT项目风险预测研究[J].价值工程,2015,(11):1-3.
[10]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,(01):115-118.
[11]包敏,简迎辉.基于PSO-BP模型地铁PPP项目调价研究[J].武汉理工大学学报(交通科学与工程版),2015,(05):1091-1094.
[12]王文寅,刘丽霞,李佳.基于PCA-BP的PPP基础设施项目风险评价研究[J].会计之友,2016,(07):56-59.
[13]黄元君,楼平,吴志军,林小峰.新型自适应RBF神经网络应用于微带天线建模[J].工程设计学报,2014,(05):426-431.
[14]王艳伟,黄宜,李靖.政府和社会资本合作(PPP)项目多目标优化模型研究[J].工程管理学报,2016,(01):48-53.
[15]叶晓甦,杨俊萍.基于多目标规划模型的PPP项目定价方式研究[J].统计与决策,2012,(06):74-77.
[16]吴孝灵,陆冰,石岿然,周静.政府补偿情景下考虑私人损失规避的PPP项目投资决策模型[J].系统管理学报,2016,(04):683-690.
[17]刘贵斌.PPP融资模式之困[J].中国金融,2016,(09):101.
[18]温来成,刘洪芳,彭羽.政府与社会资本合作(PPP)财政风险监管问题研究[J].中央财经大学学报,2015,(12):3-8.
1项目管理协会(PMI)将项目利益相关者界定为“积极参与项目,或其利益因项目的实施或完成而受到积极或消极影响的个人和组织,他们还会对项目的目标和结果施加影响”。
[2]Ebrahimnejad,S.,Mousavi,S.M.Seyrafianpour,H..Risk Identification and Assessment for Build-operate-transfer Projects:A Fuzzy Multi Attribute Decision Making Model[J].Expert Systems with Applications,2010,(37):575-586.
[3]李妍,赵蕾.新型城镇化背景下的PPP项目风险评价体系的构建——以上海莘庄CCHP项目为例[J].经济体制改革,2015,(05):17-23.
[4]Woodward D G.Use of Sensitivity Analysis in Build-ownoperate-transfer Project Evaluation[J].International Journal of Project Management,1995,13(04):239-246.
[5]张玮,张卫东.基于网络层次分析法(ANP)的PPP项目风险评价研究项目[J].项目管理技术,2012,(10):84-88.
[6]何健.基于多层次动态模糊评判的水电项目融资风险评价模型[J].广西水利水电,2008,(02):29-32.
[7]蔡晓琰,周国光.交通基础设施PPP项目智能风险评价模型研究[J].技术经济与管理研究,2016,(08):20-24.
[8]汪学清,黄嘉林,胡德俊.基于PSO-RBF神经网络的项目招标评标应用研究[J].工程管理学报,2014,(06):96-101.
[9]廖力臻,王宏辉.基于PCA-BP-GA的BOT项目风险预测研究[J].价值工程,2015,(11):1-3.
[10]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,(01):115-118.
[11]包敏,简迎辉.基于PSO-BP模型地铁PPP项目调价研究[J].武汉理工大学学报(交通科学与工程版),2015,(05):1091-1094.
[12]王文寅,刘丽霞,李佳.基于PCA-BP的PPP基础设施项目风险评价研究[J].会计之友,2016,(07):56-59.
[13]黄元君,楼平,吴志军,林小峰.新型自适应RBF神经网络应用于微带天线建模[J].工程设计学报,2014,(05):426-431.
[14]王艳伟,黄宜,李靖.政府和社会资本合作(PPP)项目多目标优化模型研究[J].工程管理学报,2016,(01):48-53.
[15]叶晓甦,杨俊萍.基于多目标规划模型的PPP项目定价方式研究[J].统计与决策,2012,(06):74-77.
[16]吴孝灵,陆冰,石岿然,周静.政府补偿情景下考虑私人损失规避的PPP项目投资决策模型[J].系统管理学报,2016,(04):683-690.
[17]刘贵斌.PPP融资模式之困[J].中国金融,2016,(09):101.
[18]温来成,刘洪芳,彭羽.政府与社会资本合作(PPP)财政风险监管问题研究[J].中央财经大学学报,2015,(12):3-8.
1项目管理协会(PMI)将项目利益相关者界定为“积极参与项目,或其利益因项目的实施或完成而受到积极或消极影响的个人和组织,他们还会对项目的目标和结果施加影响”。