Εκτίμηση των παραμέτρων ενός νέου μοντέλου ινσουλίνης -γλυκόζης

Φρέρης, Νικόλαος; Freris, Nikolaos

dc.contributor.author	Φρέρης, Νικόλαος
dc.contributor.author	Freris, Nikolaos
dc.date.accessioned	2025-06-12T05:47:59Z
dc.date.available	2025-06-12T05:47:59Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/62043
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.29739
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Γλυκόζη	el
dc.subject	Μαθηματικός υπολογισμός	el
dc.subject	Φίλτρο Kalmar	el
dc.subject	Παράμετρος φίλτρου	el
dc.subject	discrete-data linear filtering problem	en
dc.subject	Kalman filter	en
dc.subject	mathematical equations	en
dc.subject	real numbers	en
dc.subject	derivation	en
dc.title	Εκτίμηση των παραμέτρων ενός νέου μοντέλου ινσουλίνης -γλυκόζης	el
heal.type	bachelorThesis
heal.classification	Ινσουλίνη	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2004-09-01
heal.abstract	In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. The purpose of this paper is to provide a practical introduction to the discrete Kalman filter. This introduction includes a description and some discussion of the basic discrete Kalman filter, a derivation, description and some discussion of the extended Kalman filter, and a relatively simple (tangible) example with real numbers & results.	el
heal.sponsor	ΕΜΠ	el
heal.advisorName	Παπαβασιλόπουλος, Γεώργιος	el
heal.committeeMemberName	Παπαβασιλόπουλος, Γεώργιος	el
heal.academicPublisher	Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	122 σ.
heal.fullTextAvailability	false