信息与控制2011，Vol.40Issue(5)：639-645,7.DOI:10.3724/SP.J.1219.2011.00639
基于多K最近邻回归算法的软测量模型
So.Sensing Model Based on Multiple K-Nearest Neighbour Regression Algorithm
摘要
Abstract
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Key words
multiple K-nearest neighbour/ Gaussian process/ K-nearest neighbour/ soft sensing model/ adaptive affinity propagation clustering/ principal components regression分类
信息技术与安全科学引用本文复制引用
王改堂,李平,苏成利..基于多K最近邻回归算法的软测量模型[J].信息与控制,2011,40(5):639-645,7.基金项目
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) （2007T103,2009T062）
辽宁省教育厅科技计划资助项目(2008386). （2008386）
