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Abstract

This paper presents two main groups of results in the field of process model-ing; first, highlighting complexity-related properties shared by several, if not all, levels of production; second, a family of methods set up to handle problems resulting from the aforementioned properties. The presented algorithms lead up to a submodel decomposition method combining generalized feature selec-tion and artificial neural networks. Aside from theoretical presentation, practi-cal results in various levels of actual industrial production demonstrate the feasibility of the methods, suggesting that they can cope with complexity-related problems of different production levels in a uniform way.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Research Laboratory on Engineering & Management Intelligence
Depositing User:	Eszter Nagy
Date Deposited:	11 Dec 2012 15:26
Last Modified:	11 Dec 2012 15:26
URI:	https://eprints.sztaki.hu/id/eprint/4451

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