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

Authors:
C. Englund;A. Verikas
Affiliations:
Intelligent Systems Laboratory, School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, S 301 18 Halmstad, Sweden;Intelligent Systems Laboratory, School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, S 301 18 Halmstad, Sweden and Department of Applied Electronics, K ...
Venue:
Engineering Applications of Artificial Intelligence
Year:
2007

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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.