[1]孙茂伟,杨慧中.基于改进仿射传播聚类的多模型软测量建模及应用[J].南京理工大学学报(自然科学版),2016,40(02):204.[doi:10.14177/j.cnki.32-1397n.2016.40.02.012]
 Sun Maowei,Yang Huizhong.Multi-model soft-sensor modeling based on improved affinity propagation clustering algorithm and application[J].Journal of Nanjing University of Science and Technology,2016,40(02):204.[doi:10.14177/j.cnki.32-1397n.2016.40.02.012]
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基于改进仿射传播聚类的多模型软测量建模及应用
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
40卷
期数:
2016年02期
页码:
204
栏目:
出版日期:
2016-04-30

文章信息/Info

Title:
Multi-model soft-sensor modeling based on improved affinity propagation clustering algorithm and application
文章编号:
1005-9830(2016)02-0204-08
作者:
孙茂伟杨慧中
江南大学 教育部轻工过程先进控制重点实验室,江苏 无锡 214122
Author(s):
Sun MaoweiYang Huizhong
Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University,Wuxi 214122,China
关键词:
多模型 软测量 仿射传播聚类 人工鱼群算法 支持向量机
Keywords:
multi-model soft-sensor affinity propagation clustering artificial fish-swarm algorithm support vector machine
分类号:
TP274
DOI:
10.14177/j.cnki.32-1397n.2016.40.02.012
摘要:
对于多模型软测量建模,聚类效果、子模型的建模和融合方式对其模型精度有重要影响。对此,该文提出一种基于改进仿射传播聚类的多模型软测量建模方法。为提高聚类精度,在仿射传播聚类算法划分样本数据的基础上,采用人工鱼群算法对仿射传播聚类算法的偏向参数和阻尼系数寻优,同时针对距离较近类别边界处的样本再建立重叠类,采用支持向量机建立各类样本的回归子模型。分别用标准数据集仿真和工业双酚A生产装置的现场数据建模仿真,结果证明该方法是有效的。
Abstract:
For the multi-model soft-sensor modeling,its clustering results,sub-models modeling and combination of sub-models play an important role on the precision of soft-sensors.A multi-model soft-sensor modeling algorithm based on the improved affinity propagation clustering algorithm is proposed here.In order to improve the effect of clustering,an artificial fish-swarm algorithm is applied to optimize the preference parameter and the damping parameter in the affinity propagation clustering algorithm,and new overlapped clusters are built for the boundary samples located in the neighboring clusters.Then the support vector machine is used to build the regression sub-models for every cluster.The simulation results show that the algorithm for a standard data set and data of the industrial bisphenol-A production unit is effective.

参考文献/References:

[1] 曹鹏飞,罗雄麟.化工过程软测量建模方法研究进展[J].化工学报,2013,64(3):788-800.
Cao Pengfei,Luo Xionglin.Modeling of soft sensor for chemical process[J].Journal of Chemical Industry and Engineering(China),2013,64(3):788-800.
[2]王海宁,夏陆岳,周猛飞,等.过程工业软测量中的多模型融合建模方法[J].化工进展,2014,33(12):3157-3163.
Wang Haining,Xia Luyue,Zhou Mengfei,et al.Multi-model fusion modeling method for process industries soft sensor[J].Chemical Industry and Engineering Progress,2014,33(12):3157-3163.
[3]邓卫卫.多模型软测量建模方法研究及其应用[D].无锡:江南大学物联网工程学院,2012.
[4]周涛,陆惠玲.数据挖掘中聚类算法研究进展[J].计算机工程与应用,2012,48(12):100-111.
Zhou Tao,Lu Huiling.Clustering algorithm research advances on data mining[J].Computer Engineering and Applications,2012,48(12):100-111.
[5]金建国.聚类方法综述[J].计算机科学,2014,41(11A):288-293.
Jin Jianguo.Review of clustering method[J].Computer Science,2014,41(11A):288-293.
[6]沈国珍.依赖数据密度的k均值初始化调优[J].计算机工程与应用,2014,50(11):139-144.
Shen Guozhen.Improved k-means initialization method based on data density[J].Computer Engineering and Applications,2014,50(11):139-144.
[7]李嘉雯,杜文莉,李进龙,等.基于改进模糊C-均值聚类算法的乙烯裂解原料识别[J].化工学报,2013,64(12):4366-4372.
Li Jiawen,Du Wenli,Li Jinlong,et al.Feed property identification of ethylene cracking based on improved fuzzy C-mean clustering algorithm[J].Journal of Chemical Industry and Engineering(China),2013,64(12):4366-4372.
[8]张懿,刘国海,魏海峰,等.基于二次仿射传播聚类的非线性系统多模型LSSVM建模[J].控制与决策,2012,27(7):1117-1120.
Zhang Yi,Liu Guohai,Wei Haifeng,et al.Multi-model LSSVM modeling for nonlinear system based on twice affinity propagation clustering[J].Control and Decision,2012,27(7):1117-1120.
[9]张耀楠,陈传慎,康雁.基于仿射传播聚类选择的多Atlas右心室精准分割[J].东北大学学报(自然科学版),2014,35(6):795-799.
Zhang Yaonan,Chen Chuanshen,Kang Yan.Accurate segmentation of right ventricles based on multi-atlas with affinity propagation clustering selection[J].Journal of Northeastern University(Natural Science),2014,35(6):795-799.
[10]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002(11):32-38.
Li Xiaolei,Shao Zhijiang,Qian Jixin.An optimizing method based on autonomous animats:fish-swarm algorithm[J].Systems Engineering-theory & Practioce,2002(11):32-38.
[11]李晓磊.一种新型的智能优化方法——人工鱼群算法[D].杭州:浙江大学信息科学与工程学院,2003.
[12]高雷阜,赵世杰,高晶.人工鱼群算法在SVM参数优化选择中的应用[J].计算机工程与应用,2013,49(23):86-90.
Gao Leifu,Zhao Shijie,Gao Jing.Application of artificial fish-swarm algorithm in SVM parameter optimization selection[J].Computer Engineering and Applications,2013,49(23):86-90.
[13]汪丽娜,陈晓宏,李粤安,等.基于人工鱼群算法和模糊C-均值聚类的洪水分类方法[J].水利学报,2009,40(6):743-748.
Wang Lina,Chen Xiaohong,Li Yuean,et al.Method for flood classification based on fuzzy C-mean clustering and artificial fish swarm algorithm[J].Journal of Hydraulic Engineering,2009,40(6):743-748.
[14]吴文元,熊智华,吕宁,等.支持向量回归在乙烯裂解产物收率软测量中的应用[J].化工学报,2010,61(8):2046-2050.
Wu Wenyuan,Xiong Zhihua,Lv Ning,et al.Soft-sensor of product yields in ethylene pyrolysis based on support vector regression[J].Journal of Chemical Industry and Engineering(China),2010,61(8):2046-2050.
[15]Petkovic D,Shamshirband S,Saboohi H,et al.Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper[J].Applied Intelligence,2014,41(3):887-896.
[16]Liu G H,Zhou D W,Xu H X,et al.Model optimization of SVM for a fermentation soft sensor[J].Expert Systems with Appliceations,2010,37(4):2708-2713.
[17]雷小培.基于仿射传播聚类算法的改进研究[D].西安:西北大学信息科学与技术学院,2012.
[18]周世兵,徐振源,唐旭清.一种基于近邻传播算法的最佳聚类数确定方法[J].控制与决策,2011,26(8):1147-1152.
Zhou Shibing,Xu Zhenyuan,Tang Xuqing.Method for determining optimal number of clusters based on affinity propagation clustering[J].Control and Decision,2011,26(8):1147-1152.

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备注/Memo

备注/Memo:
收稿日期:2015-09-14 修回日期:2015-12-24
基金项目:国家自然科学基金(61273070); 江苏省高校优势学科建设工程资助项目
作者简介:孙茂伟(1990-),男,硕士生,主要研究方向:软测量建模,E-mail:smw906370933@163.com; 通讯作者:杨慧中(1955-),女,博士,教授,主要研究方向:复杂过程建模和优化控制,E-mail:yhz_jn@163.com。
引文格式:孙茂伟,杨慧中.基于改进仿射传播聚类的多模型软测量建模及应用[J].南京理工大学学报,2016,40(2):204-211.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2016-04-30