Guaranteed Training Set for Associative Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F17%3AA1801QXV" target="_blank" >RIV/61988987:17310/17:A1801QXV - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-58088-3_13" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-58088-3_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-58088-3_13" target="_blank" >10.1007/978-3-319-58088-3_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Guaranteed Training Set for Associative Networks

Popis výsledku v původním jazyce

The focus in this paper is on the proposal of guaranteed patterns in the training set for associative networks. All proposed patterns are pseudoortogonal and they also fulfil stability condition. Patterns were stored into the matrix using Hebb rules for associative networks. In the experimental study, we tested which from the heteroassociative Bidirectional Associative Memory (BAM) and autoassociative Hopfield network is more effective when working with the proposed patterns and what are the possibilities for Hopfield networks when working with real patterns. The comparison was made in order to recognize various damaged images using both types of associative networks. All obtained results are presented in tables or in graphs.

Název v anglickém jazyce

Guaranteed Training Set for Associative Networks

Popis výsledku anglicky

The focus in this paper is on the proposal of guaranteed patterns in the training set for associative networks. All proposed patterns are pseudoortogonal and they also fulfil stability condition. Patterns were stored into the matrix using Hebb rules for associative networks. In the experimental study, we tested which from the heteroassociative Bidirectional Associative Memory (BAM) and autoassociative Hopfield network is more effective when working with the proposed patterns and what are the possibilities for Hopfield networks when working with real patterns. The comparison was made in order to recognize various damaged images using both types of associative networks. All obtained results are presented in tables or in graphs.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Intelligent Systems and Computing

ISBN

978-331958087-6

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

11

Strana od-do

136-146

Název nakladatele

Springer Verlag

Místo vydání

Cham, Switzerland

Místo konání akce

Brno

Datum konání akce

8. 6. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—