中文摘要：

煤炭物流成本控制的必要条件就是对其成本进行预测,这样才能对煤炭物流成本进行科学合理地控制。本文提出一种鸡群算法（CSO）和支持向量回归机（SVR）结合模型,即CSO-SVR煤炭物流成本预测模型。模型利用CSO算法对SVR进行参数优化,优化后的支持向量回归机模型进行煤炭物流成本预测。通过CSOSVR模型对已有煤炭物流成本数据预测仿真。模拟结果显示,从煤炭物流成本预测精度角度出发,CSO-SVR模型预测结果优于GA-SVR、SVR、BPNN等算法。

英文摘要：

The prediction of coal logistics cost is necessary for the scientific and reasonable cost control. A cohort algorithm（ CSO） and a support vector regression（ SVR） model were proposed to optimize the parameters of SVR by using the CSO algorithm,and to achieve the purpose of logistics cost forecast. It is concluded that the CSO-SVR model is better than GA-SVR,SVR,BPNN and other methods from the point of view of prediction accuracy after CSO-SVR model being used to predict the present data.