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https://olympias.lib.uoi.gr/jspui/handle/123456789/11098Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Likas, A. | en |
| dc.contributor.author | Stafylopatis, A. | en |
| dc.date.accessioned | 2015-11-24T17:02:49Z | - |
| dc.date.available | 2015-11-24T17:02:49Z | - |
| dc.identifier.issn | 0218-0014 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11098 | - |
| dc.rights | Default Licence | - |
| dc.subject | neural computation | en |
| dc.subject | associative memory | en |
| dc.subject | random neural network | en |
| dc.subject | hebbian learning | en |
| dc.subject | spectral learning | en |
| dc.subject | threshold functions | en |
| dc.subject | queuing-networks | en |
| dc.subject | customers | en |
| dc.title | High capacity associative memory based on the random Neural Network model | 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 | 1996 | - |
| heal.abstract | In this paper the Bipolar Random Network is described, which constitutes an extension of the Random Neural Network model and exhibits autoassociative memory capabilities. This model is characterized by the existence of positive and negative nodes and symmetrical behavior of positive and negative signals circulating in the network. The network's ability of acting as autoassociative memory is examined and several techniques are developed concerning storage and reconstruction of patterns. These approaches are either based on properties of the network or constitute adaptations of existing neural network techniques. The performance of the network under the proposed schemes has been investigated through experiments showing very good storage and reconstruction capabilities. Moreover, the scheme exhibiting the best behavior seems to outperform other well-known associative neural network models, achieving capacities that exceed 0.5n where n is the size of the network. | en |
| heal.journalName | International Journal of Pattern Recognition and Artificial Intelligence | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
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