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oai:gredos.usal.es:10366/1348982025-04-30T20:38:03Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134811

Villar Flecha, José R. Sedano Franco, Javier Corchado Rodríguez, Emilio Santiago Puigpinós, Laura 2017-09-06T09:14:38Z 2017-09-06T09:14:38Z 2011 Intelligent Data Engineering and Automated Learning - IDEAL 2011 Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 6936, pp. 482-489. 978-3-642-23877-2 (Print) / 978-3-642-23878-9 (Online) 0302-9743 (Print) / 1611-3349 (Online) http://hdl.handle.net/10366/134898 It is known that the complexity inherited in most of the new real world problems, for example, the cold rolled steel industrial process, increases as the computer capacity does. Higher performance requirements with a lower amount of data samples are needed due to the costs of generating new instances, specially in those processes where new technologies arise. This study is focused on the analysis and design of a novel decision support system for an incremental steel cold shaping process, where there is a lack of knowledge of which operating conditions are suitable for obtaining high quality results. The most suitable features have been found using a wrapper feature selection method, in which genetic algorithms and neural networks are hybridized. Some facts concerning the enhanced experimentation needed and the improvements in the algorithm are drawn. en https://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivs 3.0 Unported Computer Science Soft Computing Decision Support for a Steel Sheet Incremental Cold Shaping Process info:eu-repo/semantics/conferenceObject