基于GRA-IPSO-BPNN的大中型水电项目投资估算模型研究

Investment Estimation Model of Medium-to Large-sized Hydropower Projects Based on GRA-IPSO-BPNN

水电项目投资估算对投资者安排融资和管理项目具有现实意义。在此背景下,提出基于灰色关联分析的改进粒子群优化BP神经网络(gray relation analysis,GRA;improved partical swarm optimization,IPSO;back propagation neural network,BPNN)的大中型水电项目投资估算模型。首先,借助文献回顾法和专家经验法初选影响水电项目投资的工程特征,并利用灰色关联分析筛选关键工程特征参数;然后,构建基于IPSO算法优化的BPNN模型实现水电项目投资估算;最后,通过算例分析验证了基于GRA-IPSO-BPNN的投资估算模型具有实用性和可靠性。根据实证结果,IPSO-BPNN模型相较于BPNN和PSO-BPNN,预测误差更小,可以实现大中型水电项目准确、快速、有效的投资估算。

The investment estimation of hydropower projects has practical significance for investors to arrange financing and manage projects.In this context,a hybrid model for medium-to large-sized hydropower projects investment estimation is proposed,which includes gray relation analysis(GRA),improved particle swarm optimization(IPSO),and back propagation(BP)neural network(BPNN),namely GRA-IPSO-BPNN.Firstly,a literature review and expert input are used to select the characteristics of projects that affect the hydropower project investment,and the GRA is used to screen out the key characteristic indices.Then,an IPSO-algorithm-based BPNN is constructed to realize the investment estimation of hydropower projects.Finally,the practicability and reliability of the investment estimation model are verified by applying it to an example.The new IPSO-based BPNN model has smaller prediction error than BPNN and PSO-BPNN,and can realize fast,effective and accurate investment estimation for medium-to large-sized hydropower projects.