IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION

Purpose. To consider problems of electric machines optimization within a wide range of many variables variation as well as the presence of many calculation constraints in a single-criteria optimization search tasks. Results. A structural model for optimizing electric machines of arbitrary type using...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	V. E. Pliugin, M. Sukhonos, M. Pan, A. N. Petrenko, N. Ya. Petrenko
Formato:	article
Lenguaje:	EN RU UK
Publicado:	National Technical University "Kharkiv Polytechnic Institute" 2019
Materias:	electrical machines optimization algorithm data set machine learning Microsoft Azure cloud computing Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/557f0fc1289e4d028d8741ca01102973
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:557f0fc1289e4d028d8741ca01102973
record_format	dspace
spelling	oai:doaj.org-article:557f0fc1289e4d028d8741ca011029732021-12-02T14:56:55ZIMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION10.20998/2074-272X.2019.1.042074-272X2309-3404https://doaj.org/article/557f0fc1289e4d028d8741ca011029732019-02-01T00:00:00Zhttp://eie.khpi.edu.ua/article/view/2074-272X.2019.1.04/156168https://doaj.org/toc/2074-272Xhttps://doaj.org/toc/2309-3404Purpose. To consider problems of electric machines optimization within a wide range of many variables variation as well as the presence of many calculation constraints in a single-criteria optimization search tasks. Results. A structural model for optimizing electric machines of arbitrary type using Microsoft Azure machine learning technology has been developed. The obtained results, using several optimization methods from the Microsoft Azure database are demonstrated. The advantages of cloud computing and optimization based on remote servers are shown. The results of statistical analysis of the results are given. Originality. Microsoft Azure machine learning technology was used for electrical machines optimization for the first time. Recommendations for modifying standard algorithms, offered by Microsoft Azure are given. Practical value. Significant time reduction and resources spent on the optimization of electrical machines in a wide range of variable variables. Reducing the time to develop optimization algorithms. The possibility of automatic statistical analysis of the results after performing optimization calculations. V. E. PliuginM. SukhonosM. PanA. N. PetrenkoN. Ya. PetrenkoNational Technical University "Kharkiv Polytechnic Institute"articleelectrical machinesoptimizationalgorithmdata setmachine learningMicrosoft Azurecloud computingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENRUUKElectrical engineering & Electromechanics, Iss 1, Pp 23-28 (2019)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	electrical machines optimization algorithm data set machine learning Microsoft Azure cloud computing Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	electrical machines optimization algorithm data set machine learning Microsoft Azure cloud computing Electrical engineering. Electronics. Nuclear engineering TK1-9971 V. E. Pliugin M. Sukhonos M. Pan A. N. Petrenko N. Ya. Petrenko IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
description	Purpose. To consider problems of electric machines optimization within a wide range of many variables variation as well as the presence of many calculation constraints in a single-criteria optimization search tasks. Results. A structural model for optimizing electric machines of arbitrary type using Microsoft Azure machine learning technology has been developed. The obtained results, using several optimization methods from the Microsoft Azure database are demonstrated. The advantages of cloud computing and optimization based on remote servers are shown. The results of statistical analysis of the results are given. Originality. Microsoft Azure machine learning technology was used for electrical machines optimization for the first time. Recommendations for modifying standard algorithms, offered by Microsoft Azure are given. Practical value. Significant time reduction and resources spent on the optimization of electrical machines in a wide range of variable variables. Reducing the time to develop optimization algorithms. The possibility of automatic statistical analysis of the results after performing optimization calculations.
format	article
author	V. E. Pliugin M. Sukhonos M. Pan A. N. Petrenko N. Ya. Petrenko
author_facet	V. E. Pliugin M. Sukhonos M. Pan A. N. Petrenko N. Ya. Petrenko
author_sort	V. E. Pliugin
title	IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
title_short	IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
title_full	IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
title_fullStr	IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
title_full_unstemmed	IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
title_sort	implementing of microsoft azure machine learning technology for electric machines optimization
publisher	National Technical University "Kharkiv Polytechnic Institute"
publishDate	2019
url	https://doaj.org/article/557f0fc1289e4d028d8741ca01102973
work_keys_str_mv	AT vepliugin implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT msukhonos implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT mpan implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT anpetrenko implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT nyapetrenko implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization
_version_	1718389325252526080

IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION

Ejemplares similares