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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lagaris, I. E. | en |
| dc.contributor.author | Tsoulos, I. | en |
| dc.contributor.author | Likas, A | en |
| dc.date.accessioned | 2015-11-24T17:01:00Z | - |
| dc.date.available | 2015-11-24T17:01:00Z | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10855 | - |
| dc.rights | Default Licence | - |
| dc.title | Neural Splines Exploiting Parallelism for Function Approximation Using Modular Neural Networks | 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 | 2005 | - |
| heal.abstract | We introduce the Neural Spline, that is a mathematical model built by combining a neural network and an associated Obreshkov polynomial. The neural spline has nite support and can be used as the basic element in constructing continuous mod- ular neural-based models. These models are suitable for function approximation in partitioned domains and are also amenable to e cient parallel or distributed im- plementation. Experimental results are presented for test problems in one and two dimensions which illustrate the e ectiveness of the proposed function approximation scheme. | en |
| heal.journalName | Neural Parallel and Scientific Computations | 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-2005-Neural Splines Exploiting Parallelism for Function.pdf | 413.58 kB | Adobe PDF | View/Open Request a copy |
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