Hardware and software implementation of neural network control of power systems based on the system of residual classes

The article describes the application of artificial neural networks and residual classes in the tasks of hardware-software implementation of neural networks. The comparison of existing software realizations of artificial neural networks is made. It is shown that a more effective implementation is ha...

詳細記述

保存先:

書誌詳細
主要な著者:	Tikhonov, E. E., Тихонов, Е. Е., Chebanov, K. A., Чебанов, К. А.
フォーマット:	Статья
言語:	English
出版事項:	Institute of Electrical and Electronics Engineers Inc. 2020
主題:	Computing systems Hopfield neural network Neural networks Neuroprocessor Pseudo-random generator Residual class system Application programs Hopfield neural networks
オンライン･アクセス:	https://www.scopus.com/record/display.uri?eid=2-s2.0-85078025963&origin=resultslist&sort=plf-f&src=s&st1=Hardware+and+Software+Implementation+of+Neural+Network+Control+of+Power+Systems+based+on&st2=&sid=d3dfc7d74038658a784e318139d7896a&sot=b&sdt=b&sl=103&s=TITLE-ABS-KEY%28Hardware+and+Software+Implementation+of+Neural+Network+Control+of+Power+Systems+based+on%29&relpos=2&citeCnt=0&searchTerm= https://dspace.ncfu.ru/handle/20.500.12258/11261
タグ:	タグ追加タグなし, このレコードへの初めてのタグを付けませんか!

id	ir-20.500.12258-11261
record_format	dspace
spelling	ir-20.500.12258-112612020-07-29T10:03:36Z Hardware and software implementation of neural network control of power systems based on the system of residual classes Tikhonov, E. E. Тихонов, Е. Е. Chebanov, K. A. Чебанов, К. А. Computing systems Hopfield neural network Neural networks Neuroprocessor Pseudo-random generator Residual class system Application programs Hopfield neural networks The article describes the application of artificial neural networks and residual classes in the tasks of hardware-software implementation of neural networks. The comparison of existing software realizations of artificial neural networks is made. It is shown that a more effective implementation is hardware-implemented neural networks on the basis of a programmable logic device (PLD) type FPGA of Xilinx company. To achieve greater efficiency of training and calculations it is proposed to use the system of residual classes. The article shows the results of modeling finite ring neural networks (FRNN) on the basis of FPGA with minimal hardware costs and acceptable performance. For practical approbation of the results, a model of the neural network of adaptive resonance was chosen, its adaptation for implementation on the basis of PLD type FPGA was carried out. The developed neural network is trained for the classification of input vectors of images, testing is performed, which showed 100% quality of classification of input data at their noise (up to 15%) 2020-02-07T09:24:26Z 2020-02-07T09:24:26Z 2019 Статья Tikhonov, E.E., Chebanov, K.A., Burlyaeva, V.A. Hardware and Software Implementation of Neural Network Control of Power Systems based on the System of Residual Classes // 2019 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2019. - 2019. - Номер статьи 8934139 https://www.scopus.com/record/display.uri?eid=2-s2.0-85078025963&origin=resultslist&sort=plf-f&src=s&st1=Hardware+and+Software+Implementation+of+Neural+Network+Control+of+Power+Systems+based+on&st2=&sid=d3dfc7d74038658a784e318139d7896a&sot=b&sdt=b&sl=103&s=TITLE-ABS-KEY%28Hardware+and+Software+Implementation+of+Neural+Network+Control+of+Power+Systems+based+on%29&relpos=2&citeCnt=0&searchTerm= http://hdl.handle.net/20.500.12258/11261 en 2019 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2019 application/pdf Institute of Electrical and Electronics Engineers Inc.
institution	СКФУ
collection	Репозиторий
language	English
topic	Computing systems Hopfield neural network Neural networks Neuroprocessor Pseudo-random generator Residual class system Application programs Hopfield neural networks
spellingShingle	Computing systems Hopfield neural network Neural networks Neuroprocessor Pseudo-random generator Residual class system Application programs Hopfield neural networks Tikhonov, E. E. Тихонов, Е. Е. Chebanov, K. A. Чебанов, К. А. Hardware and software implementation of neural network control of power systems based on the system of residual classes
description	The article describes the application of artificial neural networks and residual classes in the tasks of hardware-software implementation of neural networks. The comparison of existing software realizations of artificial neural networks is made. It is shown that a more effective implementation is hardware-implemented neural networks on the basis of a programmable logic device (PLD) type FPGA of Xilinx company. To achieve greater efficiency of training and calculations it is proposed to use the system of residual classes. The article shows the results of modeling finite ring neural networks (FRNN) on the basis of FPGA with minimal hardware costs and acceptable performance. For practical approbation of the results, a model of the neural network of adaptive resonance was chosen, its adaptation for implementation on the basis of PLD type FPGA was carried out. The developed neural network is trained for the classification of input vectors of images, testing is performed, which showed 100% quality of classification of input data at their noise (up to 15%)
format	Статья
author	Tikhonov, E. E. Тихонов, Е. Е. Chebanov, K. A. Чебанов, К. А.
author_facet	Tikhonov, E. E. Тихонов, Е. Е. Chebanov, K. A. Чебанов, К. А.
author_sort	Tikhonov, E. E.
title	Hardware and software implementation of neural network control of power systems based on the system of residual classes
title_short	Hardware and software implementation of neural network control of power systems based on the system of residual classes
title_full	Hardware and software implementation of neural network control of power systems based on the system of residual classes
title_fullStr	Hardware and software implementation of neural network control of power systems based on the system of residual classes
title_full_unstemmed	Hardware and software implementation of neural network control of power systems based on the system of residual classes
title_sort	hardware and software implementation of neural network control of power systems based on the system of residual classes
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2020
url	https://www.scopus.com/record/display.uri?eid=2-s2.0-85078025963&origin=resultslist&sort=plf-f&src=s&st1=Hardware+and+Software+Implementation+of+Neural+Network+Control+of+Power+Systems+based+on&st2=&sid=d3dfc7d74038658a784e318139d7896a&sot=b&sdt=b&sl=103&s=TITLE-ABS-KEY%28Hardware+and+Software+Implementation+of+Neural+Network+Control+of+Power+Systems+based+on%29&relpos=2&citeCnt=0&searchTerm= https://dspace.ncfu.ru/handle/20.500.12258/11261
work_keys_str_mv	AT tikhonovee hardwareandsoftwareimplementationofneuralnetworkcontrolofpowersystemsbasedonthesystemofresidualclasses AT tihonovee hardwareandsoftwareimplementationofneuralnetworkcontrolofpowersystemsbasedonthesystemofresidualclasses AT chebanovka hardwareandsoftwareimplementationofneuralnetworkcontrolofpowersystemsbasedonthesystemofresidualclasses AT čebanovka hardwareandsoftwareimplementationofneuralnetworkcontrolofpowersystemsbasedonthesystemofresidualclasses
_version_	1760600464024403968

Hardware and software implementation of neural network control of power systems based on the system of residual classes

類似資料