LS-SVM的核参数对概率筛筛分效率预测影响

以探索概率筛振动参数与筛分效率之间的关系，为概率筛结构的进一步改进提供指导意义为研究目的，将LS-SVM分类算法引入自同步概率筛筛分效率预测建模，探讨LS-SVM建模的可行性。基于各个不同的应用领域，可以构造不同的核函数，针对核函数需要优化特征参数的问题，应用网格搜索和交叉验证算法，对核参数的选择进行优化。通过研究得出用多项式(Poly)核函数建模对预测样本的最高预测识别率达到96.7%，采用RBF核函数建模对预测样本达到了零错分率，表明将LS-SVM算法引入概率筛筛分效率预测建模是可行的。

Research on the influence of LS-SVM kernel parameters on the accurate prediction of probabilistic screening efficiency

In order to explore the relationship between the vibration parameters of probabilistic screening and the screening efficiency, and to provide guidance for the further improvement of the probabilistic screening structure, LS-SVM classification algorithm was introduced into the self-synchronous probabilistic screening efficiency prediction modeling, and the feasibility of LS-SVM modeling was discussed. Based on different application fields, different kernel functions can be constructed. To solve the problem that kernel functions need to optimize characteristic parameters, grid search and cross validation algorithms were applied to optimize the selection of kernel parameters. Results show that the highest predictive recognition rate of the predicted samples by using Poly kernel function modeling is 96.7%, and the zero error rate of the predicted samples by using RBF kernel function modeling indicates that it is feasible to introduce LS-SVM algorithm into probabilistic screening efficiency prediction modeling.