基于粒子群与支持向量机的煤矿材料成本预测数学模型研究

Mathematical Model of Coal Mine Material Cost Prediction Based on Particle Swarm Optimization and Support Vector Machines

针对煤矿材料成本预测缺少定量分析和预测误差大等问题,提出了基于粒子群优化支持向量机的煤矿材料成本预测数学模型。并将传统的线性回归模型、支持向量机模型与提出的新数学模型对比,发现粒子群优化支持向量机模型预测精度高、误差小、结果可靠,能够为成本管理决策提供定量数据。

Aiming at the problems of lack of quantitative analysis and large prediction error in coal mine cost prediction, a mathematical model of coal mine material cost prediction based on particle swarm optimization support vector machine （SVM） is proposed.The traditional linear regression model and support vector machine model are compared with the new mathematical model.Particle swarm optimization （SVM） model has the advantages of high prediction accuracy, small error and reliable results. It can provide quantitative data for cost management decision.