THE COMPARISON OF ALGORITHMS OF RECOGNITION OF IMAGES HOPFILD’S NEURAL NETWORKS

THE COMPARISON OF ALGORITHMS OF RECOGNITION OF IMAGES HOPFILD’S NEURAL NETWORKS

The main advantage of artificial neural networks (ANN) in recognition of the cottages, is in their functioning like a human brain. The paper deals with image recognition neuron Hopfield’s networks, a comparative analysis of the recognition images by a projection’s method and the Hebb’s rule. For the...

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Main Authors: Anna Illarionovna Pavlova, Ksenya Anatolevna Bobrikova
Format: Article
Language:English
Published: Science and Innovation Center Publishing House 2016-05-01
Series:В мире научных открытий
Subjects:
искусственные нейронные сети
образы
распознавание изображений
правило Хебба
метод проекций
функция энергии сети
Online Access:http://journal-s.org/index.php/vmno/article/view/9132
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spelling doaj-46494b27e1574eab967dcc60166bf2f22021-06-02T05:31:18ZengScience and Innovation Center Publishing HouseВ мире научных открытий2072-08312307-94282016-05-010513414510.12731/wsd-2016-5-75549THE COMPARISON OF ALGORITHMS OF RECOGNITION OF IMAGES HOPFILD’S NEURAL NETWORKSAnna Illarionovna Pavlova0Ksenya Anatolevna Bobrikova1Новосибирский государственный университет экономики и управленияНовосибирский государственный университет экономики и управленияThe main advantage of artificial neural networks (ANN) in recognition of the cottages, is in their functioning like a human brain. The paper deals with image recognition neuron Hopfield’s networks, a comparative analysis of the recognition images by a projection’s method and the Hebb’s rule. For these purposes, was developed program with C# in Microsoft Visual Studio 2012. In this article to recognition for images with different levels of distortion were used. The analysis of results of recognition of images has shown that the method of projections allows to restore strongly distorted images (level of distortions up to 25–30 percent)http://journal-s.org/index.php/vmno/article/view/9132искусственные нейронные сетиобразыраспознавание изображенийправило Хеббаметод проекцийфункция энергии сети
collection DOAJ
language English
format Article
sources DOAJ
author Anna Illarionovna Pavlova
Ksenya Anatolevna Bobrikova
spellingShingle Anna Illarionovna Pavlova
Ksenya Anatolevna Bobrikova
THE COMPARISON OF ALGORITHMS OF RECOGNITION OF IMAGES HOPFILD’S NEURAL NETWORKS
В мире научных открытий
искусственные нейронные сети
образы
распознавание изображений
правило Хебба
метод проекций
функция энергии сети
author_facet Anna Illarionovna Pavlova
Ksenya Anatolevna Bobrikova
author_sort Anna Illarionovna Pavlova
title THE COMPARISON OF ALGORITHMS OF RECOGNITION OF IMAGES HOPFILD’S NEURAL NETWORKS
title_short THE COMPARISON OF ALGORITHMS OF RECOGNITION OF IMAGES HOPFILD’S NEURAL NETWORKS
title_full THE COMPARISON OF ALGORITHMS OF RECOGNITION OF IMAGES HOPFILD’S NEURAL NETWORKS
title_fullStr THE COMPARISON OF ALGORITHMS OF RECOGNITION OF IMAGES HOPFILD’S NEURAL NETWORKS
title_full_unstemmed THE COMPARISON OF ALGORITHMS OF RECOGNITION OF IMAGES HOPFILD’S NEURAL NETWORKS
title_sort comparison of algorithms of recognition of images hopfild’s neural networks
publisher Science and Innovation Center Publishing House
series В мире научных открытий
issn 2072-0831
2307-9428
publishDate 2016-05-01
description The main advantage of artificial neural networks (ANN) in recognition of the cottages, is in their functioning like a human brain. The paper deals with image recognition neuron Hopfield’s networks, a comparative analysis of the recognition images by a projection’s method and the Hebb’s rule. For these purposes, was developed program with C# in Microsoft Visual Studio 2012. In this article to recognition for images with different levels of distortion were used. The analysis of results of recognition of images has shown that the method of projections allows to restore strongly distorted images (level of distortions up to 25–30 percent)
topic искусственные нейронные сети
образы
распознавание изображений
правило Хебба
метод проекций
функция энергии сети
url http://journal-s.org/index.php/vmno/article/view/9132
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