基于网格聚类LS-SVM 的铝电解生产过程极距软测量

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.

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

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.