| 应用灰关联分析的PSO-SVR工程造价预测模型 |
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| 引用本文： | 王佼1,2,刘艳春1. 应用灰关联分析的PSO-SVR工程造价预测模型[J]. 华侨大学学报(自然科学版), 2016, 0(6): 708-713. DOI: 10.11830/ISSN.1000-5013.201606010 |
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| 作者姓名： | 王佼1 2 刘艳春1 |
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| 作者单位： | 1. 辽宁大学 商学院, 辽宁 沈阳 110036;2.东北电力大学 经济管理学院, 吉林 吉林 132012 |
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| 摘 要： | 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.
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| 关 键 词： | 工程造价 PSO-SVR预测模型 粒子群优化算法 灰关联分析 |
Prediction Model for Construction Cost Based onGrey Relational Analysis PSO-SVR |
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| WANG Jiao1,' target="_blank" rel="external">2,LIU Yanchun1. Prediction Model for Construction Cost Based onGrey Relational Analysis PSO-SVR[J]. Journal of Huaqiao University(Natural Science), 2016, 0(6): 708-713. DOI: 10.11830/ISSN.1000-5013.201606010 |
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| Authors: | WANG Jiao1,' target=" _blank" rel=" external" >2,LIU Yanchun1 |
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| Affiliation: | 1. School of Business, Liaoning University, Shenyang 110036, China; 2. School of Economics and Management, Northeast Dianli University, Jilin 132012, China |
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| Abstract: | 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. |
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| Keywords: | construction cost PSO-SVR prediction model partial swarm algorithm grey relational analysis |
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