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...

Full description

Saved in:
Bibliographic Details
Published inElectronics and electromechanics no. 1; pp. 23 - 28
Main Authors Pliuhin, V, Sukhonos, M, Pan, M, Petrenko, O, Petrenko, M
Format Journal Article
LanguageEnglish
Ukrainian
Published Kharkiv Department of Electrical Apparatus of National Technical University, Kharkiv Polytechnic Institute 01.01.2019
National Technical University, Ukraine
National Technical University "Kharkiv Polytechnic Institute"
Subjects
algorithm
Algorithms
Applied research
Artificial intelligence
Cloud computing
Electric equipment
electrical machines
Internet services
Machine learning
Methods
Microsoft Azure
Optimization
Optimization theory
Servers (Computers)
Statistical analysis
Structural models
Technology
Technology application
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2074-272X
2309-3404
DOI:10.20998/2074-272X.2019.1.04