Direct versus indirect neural control based on radial basis function networks

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Στογιάννος, Μάριος	el
dc.contributor.author	Λουκίδης, Ανδρόνικος	el
dc.contributor.author	Νίνος, Κωνσταντίνος Δ.	el
dc.contributor.author	Ζέρβα, Ευάγγελος	el
dc.date.accessioned	2015-05-23T11:02:16Z
dc.date.issued	2015-05-23
dc.identifier.uri	http://hdl.handle.net/11400/10962
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://ieeexplore.ieee.org/	en
dc.subject	Direct control
dc.subject	Indirect control
dc.subject	Model predictive control
dc.subject	Neurocontrol
dc.subject	Radial basis function
dc.subject	Άμεσος έλεγχος
dc.subject	Έμμεσος έλεγχος
dc.subject	Μοντέλο πρόβλεψης ελέγχου
dc.subject	Νευροέλεγχος
dc.subject	Ακτινική συνάρτηση βάσης
dc.title	Direct versus indirect neural control based on radial basis function networks	en
heal.type	conferenceItem
heal.classification	Technology
heal.classification	Electrical engineering
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρολογία Μηχανολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://zbw.eu/stw/descriptor/18426-4
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρολογία Μηχανολογία
heal.contributorName	Σαρίμβεης, Χαράλαμπος	el
heal.identifier.secondary	DOI: 10.1109/CEEC.2014.6958561
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.publicationDate	2014
heal.bibliographicCitation	Alexandridis, A.P., Stogiannos, M., Loukidis, A., Ninos, K.D., Zervas, E., et al. (2014) Direct versus indirect neural control based on radial basis function networks, In: Proceedings of the 6th Computer Science and Electronic Engineering Conference, CEEC 2014. University of EssexColchester, United Kingdom. 25-26 September, 2014. [online]. p. 91-96, 6958561. Available from: http://ieeexplore.ieee.org/	en
heal.abstract	This work presents a comparison between direct and indirect neural control methods based on the radial basis function (RBF) architecture. As far as direct control schemes are concerned, a novel direct inverse neural RBF controller taking into account the applicability domain criterion (INCAD) is utilized. model predictive control (MPC) formulation based on RBF networks is tested as an example of indirect method. The performances of the two control schemes are evaluated and compared on a highly nonlinear control problem, namely control of a continuous stirred tank reactor (CSTR) with multiple stable and unstable steady states. Results show that the INCAD controller is able to provide satisfactory performance, while performing almost instant calculation of the control actions. MPC on the other hand, outperforms the INCAD in terms of speed of responses, due to the built-in optimization capability; however, the lengthy procedure of solving online the optimization problem impedes the practical use of MPC on systems with fast dynamics.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	false
heal.conferenceName	6th Computer Science and Electronic Engineering Conference, CEEC 2014	en
heal.conferenceItemType	poster