基于网格聚类LS-SVM的铝电解生产过程极距软测量  

Soft-sensing of polar distance for aluminum electrolysis production process based on grid-based clustering LS-SVM

作　　者：郭俊[1] 桂卫华[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083

出　　处：《控制与决策》2012年第8期1261-1264,共4页Control and Decision

基　　金："十一五"国家支撑计划项目(2009BAE85B00)

摘　　要：针对铝电解生产过程的复杂性,建立了基于网格共享近邻聚类(GNN)最小二乘支持向量机(LS-SVM)的铝电解生产过程极距软测量模型.该模型采用GNN算法将训练集分成具有不同聚类中心的子集,对各子集分别采用LS-SVM进行训练并建立子模型,同时通过参数转化实现模型对新数据样本的动态学习.仿真结果表明,基于GNN最小二乘方法建立的铝电解极距软测量模型具有精度高、泛化性能好等特点,能够为铝电解生产过程操作优化提供实时准确的信息.Aiming at the complexity of the aluminum electrolysis production process, a soft measurement model of polar distance is proposed based on grid-based shared nearest neighbor（GNN） clustering algorithm and least square support vector machine（LS-SVM）. In this model, GNN is used to separate a whole training data set into several clusters with different centers, each subset is trained by LS-SVM and sub-models are developed to fit different hierarchical properties of the process. New sample data that represent new operation information are introduced in the model, so the model can be updated on-line. The simulation results show that the soft-sensing of polar distance based on GNN LS-SVM model can supply real-time and accurate information for the operating optimization in the aluminum electrolysis production process.

关 键 词：铝电解生产过程 极距软测量 基于网格的共享近邻聚类 最小二乘支持向量机 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]