Modern optimization problems decision made using neural network Hopfield
| Parent link: | Технологии Microsoft в теории и практике программирования: сборник трудов XII Всероссийской научно-практической конференции студентов, аспирантов и молодых ученых, г.Томск, 25-26 марта 2015 г./ Национальный исследовательский Томский политехнический университет (ТПУ), Институт кибернетики ; ред. кол. А. В. Лиепиньш [и др.]. [С. 87-89].— , 2015 |
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| Summary: | Заглавие с титульного листа. The purpose of writing this paper was to study the solution of optimization problems using Hopfield neural network in Matlab environment in order to improve that Neural network as artificial intelligence best method for provide the solutions for optimization problems. This purpose can be achieved through the following steps: 1. Formation of the basic operation of neural networks; 2. Allocation of the problems encountered when solving optimization problems using Hopfield neural network using Matlab [1]; Neural network operates cyclically. Each of the four Hopfield neural network has outputs a signal, which is input, to all other neurons but himself, however, this network cannot be taught almost anything. Network consisting of N neurons cannot remember more than ~ 0.15 * N images. Therefore, the real network should contain enough impressive number of neurons. This is one of the major flaws of the Hopfield network - a small container. In addition to all the images, do not need to be very similar to each other, or in some cases perhaps looping for recognition. |
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2015
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| Series: | Математическое моделирование и технологии высокопроизводительных вычислений |
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| Online Access: | http://earchive.tpu.ru/handle/11683/23815 http://www.lib.tpu.ru/fulltext/c/2015/C28/037.pdf |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=614547 |