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Reconstruct the Support Vectors to Improve LSSVM Sparseness for Mill Load Prediction

机译：重建支持向量以提高LSSVM稀疏度，以预测轧机负荷

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摘要

The sparse strategy plays a significant role in the application of the least square support vector machine (LSSVM), to alleviate the condition that the solution of LSSVM is lacking sparseness and robustness. In this paper, a sparse method using reconstructed support vectors is proposed, which has also been successfully applied to mill load prediction. Different from other sparse algorithms, it no longer selects the support vectors from training data set according to the ranked contributions for optimization of LSSVM. Instead, the reconstructed data is obtained first based on the initial model with all training data. Then, select support vectors from reconstructed data set according to the location information of density clustering in training data set, and the process of selecting is terminated after traversing the total training data set. Finally, the training model could be built based on the optimal reconstructed support vectors and the hyperparameter tuned subsequently. What is more, the paper puts forward a supplemental algorithm to subtract the redundancy support vectors of previous model. Lots of experiments on synthetic data sets, benchmark data sets, and mill load data sets are carried out, and the results illustrate the effectiveness of the proposed sparse method for LSSVM.
机译：稀疏策略在最小二乘支持向量机（LSSVM）的应用中起着重要作用，以缓解LSSVM解决方案缺乏稀疏性和鲁棒性的情况。本文提出了一种基于重构支持向量的稀疏方法，并将其成功应用于轧机负荷预测。与其他稀疏算法不同，它不再根据训练LSSVM的排序贡献从训练数据集中选择支持向量。取而代之的是，首先基于具有所有训练数据的初始模型获得重建数据。然后，根据训练数据集中密度聚类的位置信息，从重建的数据集中选择支持向量，遍历整个训练数据集后终止选择过程。最后，可以基于最佳重构的支持向量并随后调整超参数来构建训练模型。此外，提出了一种补充算法，以减去先前模型的冗余支持向量。进行了有关合成数据集，基准数据集和轧机负荷数据集的大量实验，结果证明了所提出的LSSVM稀疏方法的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2017年第7期|4191789.1-4191789.12|共12页
  • 作者

    Si Gangquan; Shi Jianquan; Guo Zhang; Jia Lixin; Zhang Yanbin;

  • 作者单位

    Xi An Jiao Tong Univ, Sch Elect Engn, State Key Lab Elect Insulat & Power Equipment, Xian, Peoples R China;

    Xi An Jiao Tong Univ, Sch Elect Engn, State Key Lab Elect Insulat & Power Equipment, Xian, Peoples R China;

    Xi An Jiao Tong Univ, Sch Elect Engn, State Key Lab Elect Insulat & Power Equipment, Xian, Peoples R China;

    Xi An Jiao Tong Univ, Sch Elect Engn, State Key Lab Elect Insulat & Power Equipment, Xian, Peoples R China;

    Xi An Jiao Tong Univ, Sch Elect Engn, State Key Lab Elect Insulat & Power Equipment, Xian, Peoples R China;

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