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

LI Lijuan; PAN Lei; ZHANG Shi

doi:10.3969/j.issn.0438-1157.2012.09.001

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

更新时间：2025-12-10

- Soft sensor modeling for mobility of jig bed based on AP-clustering algorithm
- CIESC Journal Issue 9, Pages: 2675-2680(2012)
- 作者机构：
  
  南京工业大学自动化与电气工程学院,江苏,南京,211816
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2012.09.001
  CLC： P13
- Online First：05 September 2012，
  
  Published：05 September 2012
- 稿件说明：
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LI Lijuan, PAN Lei, ZHANG Shi. Soft sensor modeling for mobility of jig bed based on AP-clustering algorithm[J]. CIESC Journal, 2012, (9): 2675-2680.
DOI：

LI Lijuan, PAN Lei, ZHANG Shi. Soft sensor modeling for mobility of jig bed based on AP-clustering algorithm[J]. CIESC Journal, 2012, (9): 2675-2680. DOI： 10.3969/j.issn.0438-1157.2012.09.001.

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。

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.

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