化工学报 ›› 2012, Vol. 63 ›› Issue (9): 2675-2680.DOI: 10.3969/j.issn.0438-1157.2012.09.001

• 过程系统工程 • 上一篇    下一篇

基于AP聚类算法的跳汰机床层松散度软测量建模

李丽娟, 潘磊, 张湜   

  1. 南京工业大学自动化与电气工程学院, 江苏 南京 211816
  • 收稿日期:2012-06-11 修回日期:2012-06-19 出版日期:2012-09-05 发布日期:2012-09-05
  • 通讯作者: 李丽娟
  • 作者简介:李丽娟(1976-),女,博士,副教授。
  • 基金资助:

    江苏省高校自然科学基金项目(09KJB510003);国家自然科学基金项目(61203072)。

Soft sensor modeling for mobility of jig bed based on AP-clustering algorithm

LI Lijuan, PAN Lei, ZHANG Shi   

  1. College of Automation and Electrical Engineering, Nanjing University of Technology, Nanjing 211816, Jiangsu, China
  • Received:2012-06-11 Revised:2012-06-19 Online:2012-09-05 Published:2012-09-05
  • Supported by:

    supported by the Natural Science Fund for Colleges and Universities in Jiangsu Province(09KJB510003),and the National Natural Science Foundation of China(61203072).

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

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

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