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https://olympias.lib.uoi.gr/jspui/handle/123456789/10747Full metadata record
| DC Field | Value | Language |
|---|---|---|
| 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.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2002 | - |
| 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.journalName | Methods and Applications of Artificial Intelligence | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Lagaris-2002-Piecewise neural networks for function approximation, cast in a form suitable for parallel computation.pdf | 145.7 kB | Adobe PDF | View/Open Request a copy |
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