Evolving RBF neural networks for adaptive soft-sensor design

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.date.accessioned	2015-06-03T18:44:43Z
dc.date.issued	2015-06-03
dc.identifier.uri	http://hdl.handle.net/11400/15004
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.worldscientific.com/	en
dc.subject	Adaptive modeling
dc.subject	fuzzy means
dc.subject	online learning
dc.subject	radial basis function
dc.subject	soft-sensors
dc.subject	Προσαρμοστικά μοντέλα
dc.subject	ασαφή μέσα
dc.subject	ηλεκτρονική μάθηση
dc.subject	ακτινική συνάρτηση βάσης
dc.title	Evolving RBF neural networks for adaptive soft-sensor design	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: 10.1142/S0129065713500299
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.publicationDate	2013-12
heal.bibliographicCitation	ALEXANDRIDIS, A.P. (2013). Evolving RBF neural networks for adaptive soft-sensor design. International Journal of Neural Systems. [online] 23 (6). 1350029. Available from: http://www.worldscientific.com/	en
heal.abstract	This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.	en
heal.publisher	World Scientific Publishing	en
heal.journalName	International Journal of Neural Systems	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false