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

WANG Gaitang; LI Ping; SU Chengli

Volume 40 Issue 5

Oct. 2011

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INFORMATION AND CONTROL > 2011 > 40(5): 639-645.

WANG Gaitang, LI Ping, SU Chengli. Soft-Sensing Model Based on Multiple K-Nearest Neighbour Regression Algorithm[J]. INFORMATION AND CONTROL, 2011, 40(5): 639-645.

Citation:

WANG Gaitang, LI Ping, SU Chengli. Soft-Sensing Model Based on Multiple K-Nearest Neighbour Regression Algorithm[J]. INFORMATION AND CONTROL, 2011, 40(5): 639-645.

Citation:

WANG Gaitang, LI Ping, SU Chengli. Soft-Sensing Model Based on Multiple K-Nearest Neighbour Regression Algorithm[J]. INFORMATION AND CONTROL, 2011, 40(5): 639-645.

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

1.
School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
2.
School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China

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Received Date: June 21, 2010
Revised Date: February 20, 2011
Published Date: October 19, 2011

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Abstract

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.

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[1]	李勇,邵诚.一种新的灰关联分析算法在软测量中的应用[J].自动化学报,2006,32(2):311-317.Li Y,Shao C.Application of a novel grey relation analysis algorithm to soft sensor[J].Acta Automatica Sinica,2006,32(2):311-317.
[2]	贾润达,毛志忠,常玉清.基于非线性偏鲁棒M-回归的萃余液pH值软测量[J].自动化学报,2009,35(5):583-587.Jia R D,Mao Z Z,Chang Y Q.Soft sensing for pH value of raffihate solution based on nonlinear partial robust M-regression[J].Acta Automatica Sinica,2009,35(5):583-587.
[3]	Feng R,Zhang Y J,Zhang Y Z,etal.Drifting modeling method using weighted support vector machines with Application to Soft Sensor[J].Acta Automatica Sinica,2004,30(3):436-441.
[4]	熊志化,张继承,邵惠鹤.基于高斯过程的软测量建模[J].系统仿真学报,2005,17(4):793-795.Xiong Z H,Zhang J C,Shao H H.GP-based soft sensor modeling[J].Journal of System Simulation,2005,17(4):793-795.
[5]	常玉清,邹伟,王福利,等.基于支持向量机的软测量方法研究[J].控制与决策,2005,20(11):1307-1310.Chang Y Q,Zou W,Wang F L,et al.Research on soft sensing method based on support vector machine[J].Control and Decision,2005,20(11):1307-1310.
[6]	陈如清,俞金寿.基于改进神经网络集成算法的软测量建模[J].仪器仪表学报,2008,29(6):1240-1244.Chen R Q,Yu J S.Soft sensing modeling based on improved neural network ensemble algorithm[J].Chinese Journal of Scientific Instrument,2008,29(6):1240-1244.
[7]	熊智华,王雄,徐用懋.一种利用多神经网络结构建立非线性软测量模型的方法[J].控制与决策,2000,15(2):173-176.Xiong Z H,Wang X,Xu Y M.Nonlinear software sensor modeling using mulitple neural network[J].Control and Decision,2000,15(2):173-176.
[8]	Li X L,Su H Y,Chu J.Multiple model soft sensor based on affinity propagation,Gaussian process and Bayesian committee machine[J].Chinese Journal of Chemical Engineering,2009,17(1):95-99.
[9]	罗健旭,邵惠鹤.应用多神经网络建立动态软测量模型[J].化工学报,2003,54(2):1770-1773.Luo J X,Shao H H.Developing dynamic soft sensors using multiple neural networks[J].Journal of Chemical Industry and Engineering,2003,54(2):1770-1773.
[10]	曾文华.基于多神经网络结构的常压塔侧线产品质量软测量[J].自动化仪表,2003,54(2):5-8.Zeng W H.The multiple neural network structure based soft measurement of the quality of sideline products of normal pressure tower[J].Process Automation Instrumentation,2003,54(2):5-8.
[11]	袁平,毛志忠,王福利.基于多支持向量机的软测量模型[J].系统仿真学报,2006,18(6):1458-1465.Yuan P,Mao Z Z,Wang F L.Soft sensor modeling based on multiple support vector machines[J].Journal of System Simulation,2006,18(6):1458-1465.
[12]	叶涛,朱学峰,李向阳,等.基于改进k-最近邻回归算法的软测量建模[J].自动化学报,2007,33(9):996-999.Ye T,Zhu X F,Li X Y,et al.Soft sensor modeling based on a modified k-nearest neighbor regression algorithm[J].Acta Automatica Sinica,2007,33(9):996-999.
[13]	Frey B J,Dueck D.Clustering by passing messages between data points[J].Science,2007,315(5814):972-976.
[14]	王开军,张军英,李丹,等.自适应仿射传播聚类[J].自动化学报,2007,33(12):1242-1246.Wang K J,Zhang J Y,Li D,et al.Adaptive affinity propagation clustering[J].Acta Automatica Sinica,2007,33(12):1242-1246.
[15]	Sun C Y,Wang C H,Song S,et al.A local approach of adaptive affinity propagation clustering for large scale data[C]//Proceedings of International Joint Conference on Neural Networks.Piscataway,NJ,USA:IEEE,2009:14-19.
[16]	Rasmussen C E,Williams C K I.Gaussian processes for machine learning[M].Cambridge,MA,USA:MIT Press,2006.

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

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