中文摘要：

针对高炉炼铁过程的关键工艺指标———铁水硅含量[ Si]难以直接在线检测且化验过程滞后的问题，提出一种基于稀疏化鲁棒最小二乘支持向量机（ R-S-LS-SVR）与多目标遗传参数优化的铁水[ Si]动态软测量建模方法。首先，针对标准最小二乘支持向量机（ LS-SVR）的拉格朗日乘子与误差项成正比导致最终解缺少稀疏性的问题，提取样本数据在特征空间映射集的极大无关组来实现训练样本集的稀疏化，降低建模的计算复杂度；其次，标准最小二乘支持向量机的目标函数鲁棒性不足的问题将IGGIII加权函数引入稀疏化后的最小二乘支持向量机模型进行鲁棒性改进，得到鲁棒性较强的稀疏化鲁棒最小二乘支持向量机模型；最后，针对常规均方根误差评价模型性能的不足，提出从建模误差与估计趋势评价建模性能的多目标评价指标。在此基础上，利用非支配排序的带有精英策略的多目标遗传算法优化模型参数，从而获得具有最优参数的铁水[ Si]在线软测量模型。工业实验及比较分析验证了所提方法的有效性和先进性。

英文摘要：

To solve the problem that the parameter of silicon content （ [ Si] ） in hot mental is difficult to be directly detected and obtained by manual analysis with large time delay, a method of sparse and robust least squares support vector regression （ R-S-LS-SVR） was proposed to establish a dynamic model of [ Si] with the help of the multi-objective genetic optimization of model parame-ters. First, owing to the issue that the Lagrange multiplier of the standard least squares support vector machine （ LS-SVR） is directly proportional to the error term and solves the lack of sparsity, the maximal independent set of sample data in the feature space mapping set was extracted to realize the sparse of the training sample set and reduce the computational complexity of modeling. Next, in view of the problem that the standard least squares support vector machine has no regularization term, a method to improve the modeling ro-bustness was proposed by introducing the IGGIII weighting function into the obtained sparse least squares support vector regression （ S-LS-SVR） model. Last, the multi-objective evaluation index that synthesizes the modeling residue and the estimated trend was＆nbsp;presented to compensate for the deficiency of the single root mean square error （ RMSE） index. Based on those, an on-line soft sensor model of hot metal [ Si] with the optimal parameters was obtained by using the multi-objective genetic algorithm （ NSGA-II） with the non-dominated sort and elitist strategy. Industrial verification and analysis show the effectiveness and superiority of the proposed method.