改进粒子群算法优化支持向量机的工程造价预测

工程造价预测一直是工程管理研究中的重点,针对工程造价预测中的支持向量机参数优化问题,提出一种改进粒子群算法优化支持向量机的工程造价预测模型(IPSO-SVM).首先收集工程造价数据,并对其进行归一化处理,然后采用支持向量机对工程造价的训练样本进行学习,并采用改进粒子群算法对支持向量机的核函数参数进行优化,最后采用Matlab 2012工具箱对工程造价进行仿真实验.实验结果表明,IPSO-SVM有效提高工程造价的预测精度,预测结果具有一定的实际应用价值.

Project Cost Forecasting Based on Improved Particle Swarm Algorithm Optimizing Support Vector Machine

Project cost forecasting is a key point in the research on project management, in view of support vector machine parameter optimization problem in project cost forecasting, a new project cost forecasting model (IPSO-SVM) is proposed, which is based on the improved particle swarm optimizing supporting vector machine. Firstly, project cost data is collected and processed, and then support vector machine is used to learn for training samples in which improved particle swarm algorithm is used to optimize kernel function parameters of support vector machine, At last, the simulation experiment is used to test the performance of project cost forecasting by using Matlab 2012. The experimental results show that IPSO-SVM can effectively improve the forecasting accuracy of project cost, and the forecasting results have some practical application values.