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 |
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| Year | 2007 |
| PublicationType | Journal Paper |
| Journal | Engineering applications of artificial intelligence |
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| 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. |
