A neural network approach for the prediction of the refractive index based on experimental data

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Χονδροδήμα, Ευαγγελία	el
dc.contributor.author	Μουτζούρης, Κωνσταντίνος Ι.	el
dc.contributor.author	Τριάντης, Δήμος Α.	el
dc.date.accessioned	2015-05-16T10:47:42Z
dc.date.available	2015-05-16T10:47:42Z
dc.date.issued	2015-05-16
dc.identifier.uri	http://hdl.handle.net/11400/10511
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://link.springer.com/	en
dc.subject	Ethanol water mixtures
dc.subject	Experimental data
dc.subject	Innovative algorithms
dc.subject	Neural network model
dc.subject	Neural network training
dc.subject	Nonlinear nature
dc.subject	Prediction accuracy
dc.subject	Real measurements
dc.subject	Sellmeier equation
dc.subject	Μίγματα αιθανόλης νερού
dc.subject	Πειραματικά δεδομένα
dc.subject	Καινοτόμοι αλγόριθμοι
dc.subject	Μοντέλο νευρωνικού δικτύου
dc.subject	Εκπαίδευση νευρωνικού δικτύου
dc.subject	Ακρίβεια πρόβλεψης
dc.subject	Πραγματικές μετρήσεις
dc.subject	Εξίσωση Sellmeier
dc.title	A neural network approach for the prediction of the refractive index based on experimental data	en
heal.type	journalArticle
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.identifier.secondary	DOI: 10.1007/s10853-011-5868-y
heal.language	en
heal.access	campus
heal.publicationDate	2012-01
heal.bibliographicCitation	ALEXANDRIDIS, A.P., CHONDRODIMA, E., MOUTZOURIS, K.I. & TRIANTIS, D.A. (2012). A neural network approach for the prediction of the refractive index based on experimental data. Journal of Materials Science. [Online] 47 (2). p. 883-891. Available from: http://link.springer.com/[Accessed 24/08/2011]	en
heal.abstract	This article presents a systematic approach for correlating the refractive index of different material kinds and forms with experimentally measured inputs like wavelength, temperature, and concentration. The correlation is accomplished using neural network models, which can deal effectively with the nonlinear nature of the problem without requiring a predefined form of equation, while taking into account all the parameters affecting the refractive index. The proposed methodology employs the powerful radial basis function network architecture and the neural network training procedure is accomplished using an innovative algorithm, which provides results with increased prediction accuracy. The methodology is applied to two cases, involving the estimation of the refractive index of semiconductor material crystals and an ethanol-water mixture and the results show that the refractive index predictions are accurate approximately to the same number of decimal places as the real measurements. Comparisons with other neural network training methods, but also with empirical forms like the Sellmeier equation, highlight the superiority of the proposed approach.	en
heal.publisher	Springer Verlag	en
heal.journalName	Journal of Materials Science	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true