基于多K最近邻回归算法的软测量模型  

Soft-Sensing Model Based on Multiple K-Nearest Neighbour Regression Algorithm

作　　者：王改堂[1] 李平[2] 苏成利[2] 

机构地区：[1]西北工业大学自动化学院,陕西西安710072 [2]辽宁石油化工大学信息与控制工程学院,辽宁抚顺113001

出　　处：《信息与控制》2011年第5期639-645,共7页Information and Control

基　　金：辽宁省高校创新团队支持计划资助项目(2007T103;2009T062);辽宁省高等学校优秀人才支持计划资助项目(2008RC32);辽宁省教育厅科技计划资助项目(2008386)

摘　　要：针对单一模型预测精度较低的问题,提出多K最近邻回归算法(MKNN)的软测量建模方法.该方法采用高斯过程选择软测量模型的辅助变量,通过自适应仿射传播聚类方法将输入样本数据分成多组数据,对每组数据用K最近邻回归(KNN)算法建立子模型,各个子模型的预测输出通过主元回归(PCR)方法连接.用该方法建立粗汽油干点软测量模型,仿真研究表明,该算法的预测精度和泛化能力优于单KNN模型.A soft-sensing modeling method is proposed based on multiple K-nearest neighbor（MKNN） regression algorithm to solve the problem that a single model has lower prediction precision.The method adopts Gaussian process to choose secondary variable for soft sensing model.Then,an adaptive affinity propagation clustering method is adopted to divide the input samples data into several groups,and sub-models are built by KNN in each group.The predictive outputs of sub-models are combined by principal components regression（PCR）.The proposed MKNN method is used in soft sensing modeling of the end point of crude gasoline.Compared with single KNN modeling,the simulation results show that the algorithm has better prediction precision and generalization performance.

关 键 词：多K最近邻 高斯过程 K最近邻 软测量模型 自适应仿射传播聚类 主元回归 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]