Learning fuzzy grey cognitive maps using nonlinear hebbian-based approach

dc.contributor.author	Παπαγεωργίου, Ελπινίκη Ι.	el
dc.contributor.author	Salmeron, Jose L.	en
dc.date.accessioned	2015-05-22T12:32:37Z
dc.date.available	2015-05-22T12:32:37Z
dc.date.issued	2015-05-22
dc.identifier.uri	http://hdl.handle.net/11400/10891
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://www.sciencedirect.com/science/article/pii/S0888613X11001356	en
dc.subject	Biomedical engineering
dc.subject	Fuzzy cognitive maps
dc.subject	Βιοϊατρική μηχανική
dc.subject	Ασαφή γνωστικά
dc.title	Learning fuzzy grey cognitive maps using nonlinear hebbian-based approach	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Computer science
heal.classification	Τεχνολογία
heal.classification	Πληροφορική
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://data.seab.gr/concepts/77de68daecd823babbb58edb1c8e14d7106e83bb
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Πληροφορική
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.identifier.secondary	doi:10.1016/j.ijar.2011.09.006
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2012
heal.bibliographicCitation	Papageorgiou, E. and Salmeron, J. (January 2012). Learning fuzzy grey cognitive maps using nonlinear hebbian-based approach. International Journal of Approximate Reasoning. 53(1). pp. 54-65. Elsevier Inc: 2012. Available from: http://www.sciencedirect.com/science/article/pii/S0888613X11001356 [Accessed 01/10/2011]	en
heal.abstract	Recently, Fuzzy Grey Cognitive Maps (FGCM) has been proposed as a FCM extension. It is based on Grey System Theory, that it has become a very effective theory for solving problems within environments with high uncertainty, under discrete small and incomplete data sets. The proposed approach of learning FGCMs applies the Nonlinear Hebbian based algorithm determine the success of radiation therapy process estimating the final dose delivered to the target volume. The scope of this research is to explore an alternative decision support method using the main aspects of fuzzy logic and grey systems to cope with the uncertainty inherent in medical domain and physicians uncertainty to describe numerically the influences among concepts in medical domain. The Supervisor-FGCM, trained by NHL algorithm adapted in FGCMs, determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Three clinical case studies were used to test the proposed methodology with meaningful and promising results and prove the efficiency of the NHL algorithm for FGCM approach.	en
heal.publisher	Elsevier Inc	en
heal.journalName	International Journal of Approximate Reasoning	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true