Piecewise neural networks for function approximation, cast in a form suitable for parallel computation

dc.contributor.author	Tsoulos, I. G.	en
dc.contributor.author	Lagaris, I. E.	en
dc.contributor.author	Likas, A. C.	en
dc.date.accessioned	2015-11-24T17:00:19Z
dc.date.available	2015-11-24T17:00:19Z
dc.identifier.issn	0302-9743	-
dc.identifier.uri	https://olympias.lib.uoi.gr/jspui/handle/123456789/10747
dc.rights	Default Licence	-
dc.subject	optimization	en
dc.title	Piecewise neural networks for function approximation, cast in a form suitable for parallel computation	en
heal.abstract	We present a technique for function approximation in a partitioned domain. In each of the partitions a form containing a Neural Network is utilized with parameterized boundary conditions. This parameterization renders feasible the parallelization of the computation. Conditions of continuity across the partitions are studied for the function itself and for a number of its derivatives. A comparison is made with traditional methods and the results axe reported.	en
heal.access	campus	-
heal.fullTextAvailability	TRUE	-
heal.journalName	Methods and Applications of Artificial Intelligence	en
heal.journalType	peer reviewed	-
heal.language	en	-
heal.publicationDate	2002	-
heal.recordProvider	Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής	el
heal.type	journalArticle	-
heal.type.el	Άρθρο Περιοδικού	el
heal.type.en	Journal article	en

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