基于RBF网络的水电BOT项目投资风险评估
发布时间：2018-11-06 11:54
【摘要】：为提高水电BOT项目投资风险评估水平,针对其投资风险影响因素的非线性、模糊性及不确定性等特点,构建水电BOT项目投资风险识别矩阵,应用径向基函数神经网络建立水电BOT项目的投资风险评估RBF模型;运用Matlab建构风险评估模拟程序并预测出项目的投资风险等级,为风险管理提供定量计算方法。算例仿真结果得出该项目的综合风险评估值为0.581 6,与实际情况相符,表明该模型在评估水电BOT项目投资风险上具有可行性。
[Abstract]:In order to improve the investment risk assessment level of hydropower BOT project, aiming at the nonlinearity, fuzziness and uncertainty of the influencing factors of investment risk, the investment risk identification matrix of hydropower BOT project is constructed. The radial basis function neural network is used to establish the investment risk assessment RBF model of hydropower BOT project. The risk assessment simulation program is constructed by using Matlab and the investment risk grade of the project is forecasted, which provides a quantitative calculation method for risk management. The simulation results show that the comprehensive risk assessment value of the project is 0.581, which is consistent with the actual situation, which indicates that the model is feasible in evaluating the investment risk of hydropower BOT project.
【作者单位】： 三峡大学水利与环境学院;
【分类号】：TV51
本文编号：2314184
[Abstract]:In order to improve the investment risk assessment level of hydropower BOT project, aiming at the nonlinearity, fuzziness and uncertainty of the influencing factors of investment risk, the investment risk identification matrix of hydropower BOT project is constructed. The radial basis function neural network is used to establish the investment risk assessment RBF model of hydropower BOT project. The risk assessment simulation program is constructed by using Matlab and the investment risk grade of the project is forecasted, which provides a quantitative calculation method for risk management. The simulation results show that the comprehensive risk assessment value of the project is 0.581, which is consistent with the actual situation, which indicates that the model is feasible in evaluating the investment risk of hydropower BOT project.
【作者单位】： 三峡大学水利与环境学院;
【分类号】：TV51
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