应用灰关联分析的PSO-SVR工程造价预测模型

Prediction Model for Construction Cost Based on Grey Relational Analysis PSO-SVR

为了准确预测与控制工程造价水平,提出一种基于灰关联分析(GRA)与粒子群优化(PSO)的支持向量回归机(SVR)组合预测模型.将GRA提取的工程造价主要指标向量输入PSO-SVR模型预测造价,采用PSO优化的SVR模型进行工程造价预测,对比分析PSO-SVR模型和其他智能模型,对某一地区相同输电工程进行造价预测.结果表明:基于灰关联分析的PSO-SVR模型的造价预测效果更理想,预测精度更高.

In order to accurately predict and control construction cost,we propose a forecasting model based on grey relational analysis（GRA）and support vector regression（SVR）integrated with particle swarm optimization（PSO）.Key indicators of construction cost are firstly extracted using grey relational analysis（GRA）and then input into the PSO-SVR model to make predictions.The construction costs of the same electricity transmission projects predicted by the PSO-SVR model and other intelligent models were compared.The results show that the PSO-SVR model based on GRA is more accurate.