Abstract: Mobility of bed is the important factor for jigging separation process.A soft sensing modeling method based on the least squares-support vector machine(LS-SVM)is developed to deal with the problem that mobility cannot be measured directly online.In full consideration of highly nonlinear and strong coupling characteristic of separation process,an LS-SVM multi-model method based on affinity propagation(AP)clustering is presented and applied to avoid bad accuracy of single model expressing multiple working positions.In the presented method,AP-clustering algorithm is used to cluster training samples.Then,the sub-models are trained by LS-SVM.Finally,the predicted values of the testing samples are estimated by the sub-models after it is classified by switchover.Simulation results show that a better prediction for mobility of jig bed is obtained by the LS-SVM multi-model method based on AP-clustering algorithm.

Key words: jig, mobility of bed, AP-clustering, multi-model, LS-SVM

摘要： 松散度是跳汰分选过程的重要影响因素,针对其难以用仪器在线检测的问题,提出采用最小二乘支持向量机(LS-SVM)的软测量建模方法。在充分考虑分选过程高度非线性及强耦合性的基础上,为避免单模型建模回归精度差和泛化能力弱的问题,提出采用基于仿射传播(AP)聚类的LS-SVM多模型建模算法进行床层松散度软测量建模。首先采用AP算法对样本数据进行聚类划分,再用LS-SVM的方法对子类样本分别建立子模型,最后通过子模型切换策略得到系统输出。仿真实验表明,基于AP聚类算法的LS-SVM软测量建模算法能够更好地预测跳汰机床层松散度。

关键词: 跳汰机, 床层松散度, AP聚类算法, 多模型, 最小二乘支持向量机