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...
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| Главные авторы: | , , , |
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| Формат: | Статья |
| Язык: | English |
| Опубликовано: |
Institute of Electrical and Electronics Engineers Inc.
2020
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| Темы: | |
| Online-ссылка: | 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 |
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| Краткое описание: | 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%) |
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