|Table of Contents|

Soft sensor modeling method based on improved expandingsearching clustering algorithm(PDF)

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

Issue:
2017年05期
Page:
574-
Research Field:
Publishing date:

Info

Title:
Soft sensor modeling method based on improved expandingsearching clustering algorithm
Author(s):
Zhang Sunli; Yang Huizhong
Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education),Jiangnan University,Wuxi 214122,China
Keywords:
densities;  threshold;  Gaussian process regression;  expanding searching clusting algorithm;  soft sensor modeling
PACS:
TP274
DOI:
10.14177/j.cnki.32-1397n.2017.41.05.006
Abstract:
An improved expanding searching clustering algorithm is proposed to overcome the shortcomings of the traditional clustering methods relying on data space distribution and prior knowledge too much.In consideration of the effects of the sample density on the searching radlus,the improved algorithm selects different searching radius according to the density of each sample point.For all sample distribution shapes,the threshold value is applied to choose different clustering methods relying on different density points.Sample data is clustered by using the improved expanding searching clustering algorithm.All soft sensor models are built up by Gaussian process regression(GPR).The final model is formed by using the switch fusion mode according to the results of clustering.A sample of a bisphenol-A production crystallization unit is applied to make a simulation for building the soft-sensor model of the phenol concentration at the exit device and the good experiment results are obtained.

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Last Update: 2017-09-30