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High order neural networks to control manufacturing systems - a comparison study

Rovithakis, George A., 1968-, Gaganis Vasileios, Perrakis, Stelios, Christodoulou Manolis

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URIhttp://purl.tuc.gr/dl/dias/078E7339-3C35-4329-A994-B2FAC9CBE623-
Identifierhttps://doi.org/10.1109/CDC.1998.757868-
Identifierhttp://ieeexplore.ieee.org/document/757868/?denied-
Languageen-
Extent2 pagesen
TitleHigh order neural networks to control manufacturing systems - a comparison studyen
CreatorRovithakis, George A., 1968-en
CreatorGaganis Vasileiosen
CreatorΓαγανης Βασιλειοςel
CreatorPerrakis, Steliosen
CreatorChristodoulou Manolisen
CreatorΧριστοδουλου Μανωληςel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryIn this paper the neuro adaptive scheduling methodology is evaluated by comparing its performance with conventional schedulers, through simulation studies. The case study chosen constitutes an existing manufacturing cell, which can be viewed as a highly complex nonacyclic FMS, with extremely heterogenous part processing times. The results reveal superiority of our algorithm in terms of backlogging and inventory cost, system stability and work-in-process en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-07-
Date of Publication1998-
SubjectArtificial neural networksen
SubjectNets, Neural (Computer science)en
SubjectNetworks, Neural (Computer science)en
SubjectNeural nets (Computer science)en
Subjectneural networks computer scienceen
Subjectartificial neural networksen
Subjectnets neural computer scienceen
Subjectnetworks neural computer scienceen
Subjectneural nets computer scienceen
Bibliographic CitationG. Rovithakis, V. Gaganis, S. Perrakis and M. Christodoulou, “High order neural networks to control manufacturing systems - a comparison study”, in 37th IEEE Conference on Decision and Control, 1998, pp. 2736-2737. doi: 10.1109/CDC.1998.757868en

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