|Table of Contents|

Multi-model soft-sensor modeling based on improved affinity propagation clustering algorithm and application

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

Issue:
2016年02期
Page:
204-
Research Field:
Publishing date:

Info

Title:
Multi-model soft-sensor modeling based on improved affinity propagation clustering algorithm and application
Author(s):
Sun MaoweiYang Huizhong
Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University,Wuxi 214122,China
Keywords:
multi-model soft-sensor affinity propagation clustering artificial fish-swarm algorithm support vector machine
PACS:
TP274
DOI:
10.14177/j.cnki.32-1397n.2016.40.02.012
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.

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Last Update: 2016-04-30