基于IPSO_LS-SVM 的国防科研项目概算价格估算研究

为解决国内在估算方法选择和模型性能优化上存在的问题,利用改进的粒子群算法优化最小二乘支持向量机(least squares support vector machine,LS-SVM)的参数选择方法,对国防科研项目概算价格估算进行研究.依据最小二乘支持向量机原理,通过优化其参数选择方法,建立了IPSO_LS-SVM概算价格估算模型,并对其进行模型训练和结果验证.结果表明:IPSO_LS-SVM方法估算精度更高,参数寻优速度更快,其估算模型具有有效性和优越性.

Research on National Defense Project Development-cost EvaluationBased on IPSO_LS-SVM

To solve the existing problems of forecasting method selection and model performance optimization in China, a method of optimizing the parameters selection for the least squares support vector machine (LS-SVM) with Improved Particle Swarm Optimization(IPSO) is proposed to carry out research on national defense project development-cost evaluation. Based on the principle of least square support vector machine, the estimation model of IPSO_LS-SVM is established by optimizing its parameter selection method, and the model training and result verification are carried out. The results show that the IPSO_LS-SVM method has higher precision and faster parameter optimization, and its estimation model is effective and advantageous.