【摘要】 为实现转炉终点钢水磷含量的精准预报，首先采用核主成分分析(KPCA)对数据进行降维处理，再提出改进的蜣螂优化算法(IDBO)来优化最小二乘支持向量机(LSSVM),最后建立KPCA-IDBO-LSSVM预测模型来进行转炉终点钢水磷含量预测。将KPCA-IDBO-LSSVM终点磷含量预测结果与LSSVM、IDBO-LSSVM以及多种其他模型进行对比，结果表明，KPCA与IDBO的加入均明显地提升了预测效果，KPCA-IDBO-LSSVM的终点磷含量预测误差在±0.003%内的命中率达到了90%,为冶炼带来了具有实际意义的帮助。更多还原

【Abstract】 In order to achieve accurate prediction of phosphorus content in molten steel at the end point of converter, kernel principal component analysis(KPCA) was used to reduce dimensionality of data, and then an improved dung beetle optimization(IDBO) algorithm was proposed to optimize least square support vector machine(LSSVM). Finally, KPCA-IDBO-LSSVM prediction model is established to predict the phosphorus content of steel at the end of converter. The prediction results of terminal phosphorus content of KPCA-IDBO-LSSVM were compared with those of LSSVM, IDBO-LSSVM and other models. The results showed that the addition of KPCA and IDBO significantly improved the prediction effect. The prediction error of terminal phosphorus content of KPCA-IDBO-LSSVM reaches 90% within ±0.003%, which brings practical help for smelting.更多还原