Difference between revisions of "Publications:A SOM-based data mining strategy for adaptive modelling of an offset lithographic printing process"

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|Name=Englund, Cristofer [cren] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938]);Verikas, Antanas [av] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941])
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|Name=Englund, Cristofer (crieng) (0000-0002-1043-8773) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Intelligenta system (IS-lab) (3941))
 
|Title=A SOM-based data mining strategy for adaptive modelling of an offset lithographic printing process
 
|Title=A SOM-based data mining strategy for adaptive modelling of an offset lithographic printing process
 
|PublicationType=Journal Paper
 
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|CreatedDate=2008-01-22
 
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|LastUpdated=2012-09-28
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|LastUpdated=2016-02-24
 
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Latest revision as of 21:42, 30 September 2016

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Title A SOM-based data mining strategy for adaptive modelling of an offset lithographic printing process
Author
Year 2007
PublicationType Journal Paper
Journal Engineering applications of artificial intelligence
HostPublication
Conference
DOI http://dx.doi.org/10.1016/j.engappai.2006.07.004
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:238301
Abstract

This paper is concerned with a SOM-based data mining strategy for adaptive modelling of a slowly varying process. The aim is to follow the process in a way that makes a representative up-to-date data set of a reasonable size available at any time. The technique developed allows analysis and filtering of redundant data, detection of the need to update the process models and the core-module of the system itself and creation of process models of adaptive, data-dependent complexity. Experimental investigations performed using data from a slowly varying offset lithographic printing process have shown that the tools developed can follow the process and make the necessary adaptations of the data set and the process models. A low-process modelling error has been obtained by employing data-dependent committees for modelling the process.