|Table of Contents|

Soft sensor modeling based on adaptive Isomap algorithm(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2019年03期
Page:
269-274
Research Field:
Publishing date:

Info

Title:
Soft sensor modeling based on adaptive Isomap algorithm
Author(s):
Ji WenpengYang Huizhong
Key Laboratory of Advanced Process Control for Light Industry,Ministry of Education,Jiangnan University,Wuxi 214122,China
Keywords:
adaptive algorithm isometric mapping algorithm neighborhood graph construction Euclidean distance soft sensor Gaussian regression process
PACS:
TP274
DOI:
-
Abstract:
An adaptive neighborhood construction method is proposed for the neighborhood graph construction in the isometric mapping(Isomap)algorithm. The sample similarity coefficient is calculated by using the Euclidean distance. A density exponential function is constructed based on the local density and average density of each sample. The neighbor number of samples is adjusted adaptively according to the density exponential function to construct a reasonable neighborhood graph. A model is developed by using the Gaussian process regression(GPR). This method is applied to the soft sensor modeling of a Bisphenol A production device. The simulation results show that the GPR model based on the adaptive Isomap algorithm has higher estimation accuracy than the Isomap-GPR model,and the root mean square error(RMSE)of the model is reduced by about 15%.

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 peocess[J]. CIESC Journal,2013,64(3):788-800.
[2]杨慧中,邓玉俊. 基于自适应增强算法的支持向量机组合模型[J]. 控制与决策,2011,26(2):316-319.
Yang Huizhong,Deng Yujun. Compositional model of SVM based on AdaBoosting algorithm[J]. Control and Decision,2011,26(2):316-319.
[3]钟怀兵,熊伟丽. 一种带奇异点检测和补偿的GPR在线软测量方法[J]. 南京理工大学学报,2017,41(4):503-510.
Zhong Huaibing,Xiong Weili. Online soft sensor method based on GPR with test and compensation for singular point[J]. Journal of Nanjing University of Science and Technology,2017,41(4):503-510.
[4]孙茂伟,杨慧中. 基于改进Bagging算法的高斯过程集成软测量建模[J]. 化工学报,2016,67(4):1386-1391.
Sun Maowei,Yang Huizhong. Gaussian process ensemble soft-sensor modeling based on improved Bagging algorithm[J]. CIESC Journal,2016,67(4):1386-1391.
[5]Tenenbaum J B,de Silva V,Langford J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science,2000,290(22):2319-2323.
[6]Roweis S T,Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science,2000,290(5500):2323-2326.
[7]李荣雨,王立明. 基于ISOMAP-ELM的软测量建模及化工应用[J]. 计量学报,2016,37(5):548-552.
Li Rongyu,Wang Liming. Soft measurement modeling and chemical application based on ISOMAP-ELM neural network[J]. Acta Metrologica Sinica,2016,37(5):548-552.
[8]薄翠梅,韩晓春,易辉,等. 基于聚类选择K近邻的LLE算法及故障检测[J]. 化工学报,2016,67(3):925-930.
Bo Cuimei,Han Xiaochun,Yi Hui,et al. Neighborhood selection of LLE based on cluster for fault detection[J]. CIESC Journal,2016,67(3):925-930.
[9]Zhang Ting,Du Yi,Huang Tao,et al. Stochastic simulation of patterns using ISOMAP for dimensionality reduction of training images[J]. Computers and Geosciences,2015,79:82-93.
[10]Yang Bo,Xiang Ming,Zhang Yupei. Multi-manifold discriminant Isomap for visualization and classification[J]. Pattern Recognition,2016,55:215-230.
[11]黄宏臣,韩振南,张倩倩,等. 基于拉普拉斯特征映射的滚动轴承故障识别[J]. 振动与冲击,2015,34(5):128-134,144.
Huang Hongchen,Han Zhennan,Zhang Qianqian,et al. Method of fault diagnosis for rolling bearings based on Laplacian eigenmap[J]. Journal of Vibraion and Shock,2015,34(5):128-134,144.
[12]Chen Chun,Zhang Lijun,Bu Jiajun,et al. Constrained Laplacian eigenmap for dimensionality reduction[J]. Neurocomputing,2010,73(4/6):951-958.
[13]王健,冯健,韩志艳. 基于流形学习的局部保持PCA算法在故障检测中的应用[J]. 控制与决策,2013,28(5):683-687.
Wang Jian,Feng Jian,Han Zhiyan. Locally preserving PCA method based on manifold learning and its application in fault detection[J]. Control and Decision,2013,28(5):683-687.
[14]Murota K,Shioura A. Dijkstra’s algorithm and L-concave function maximization[J]. Mathematical Programming,2014,145(1/2):163-177.
[15]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.
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(2):204-211.
[16]仓文涛,杨慧中. 基于改进随机梯度Boosting算法的软测量建模[J]. 化工学报,2017,68(3):970-975.
Cang Wentao,Yang Huizhong. A soft sensor modeling method based on modified stochastic gradient Boosting[J]. CIESC Journal,2017,68(3):970-975.
[17]梅从立,杨铭,刘国海. 基于证据合成的高斯过程回归多模型软测量方法[J]. 化工学报,2015,66(11):4555-4564.
Mei Congli,Yang Ming,Liu Guohai. A multi-model based soft sensor using evidence theory and Gaussian process regression[J]. CIESC Journal,2015,66(11):4555-4564.

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Last Update: 2019-06-30