基于粒子群优化支持向量机的冲天炉铁液质量预测

冲天炉铁液质量预测是一项复杂而有难度的技术,受到很多因素的影响。文章提出了基于粒子群优化支持向量机（PSO-SVM）的冲天炉铁液质量预测方法,即将粒子群优化算法（PSO）用于SVM参数优化。它不仅具有很强的全局搜索能力,而且容易实现。经实验结果证明,PSO-SVM的预测输出与实测数据基本一致,其预测精度高于普通的SVM,所有的预测误差都远小于5%的工程许可误差。

Cupola Molten Iron Quality Forecasting Model of Support Vector Machine Based on Particle Swarm Optimization

Prediction of the Quality of Cupola Molten Iron is an intricate task affected by many factors.Therefore,its prediction accuracy is worth attention.The proposed PSO-SVM method is applied to predict the Quality of Cupola Molten Iron in the paper,among which particle swarm optimization（PSO） is used to determine free parameters of support vector machine.The method not only has strong global search capability,but also is very easy to implement.Prediction of Quality of Cupola Molten Iron examples are used to illustrate the performance of proposed PSO-SVM method.The experimental results indicates that the PSO-SVM method can achieve the nearly same result as measured data and higher diagnostic accuracy than normal SVM consequently,w hich is far less than 5% o f the project license error.