Guaranteed Training Set for Associative Networks

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Guaranteed Training Set for Associative Networks
Original language description
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.
Czech name
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Czech description
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Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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