|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 Wenpeng; Yang 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%.

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Last Update: 2019-06-30