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

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Title A SOM-based data mining strategy for adaptive modelling of an offset lithographic printing process
Author Cristofer Englund and Antanas Verikas
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.