Skip to content
DSML.KZ
Publications

Article · Journal of open systems evolution problems · January 16, 2023

КЛАССИФИКАЦИЯ СПЕКТРОВ НОРМАЛЬНЫХ ЗВЕЗД ГЛАВНОЙ ПОСЛЕДОВАТЕЛЬНОСТИ НЕЙРОННОЙ СЕТЬЮ ГЛУБОКОГО ОБУЧЕНИЯ

S.A. Sarmanbetov, А.А. Khokhlov, Е.Т. Kozhagulov, М.К. Ibraimov, М.К. Ибраимов, М.К. Ибраимов

S.a. Sarmanbetov
Read on peos.kaznu.kz

Abstract

ence stars and gives a brief description of the research method. The сonvolutional Neural Network (CNN) was tested in a classification on two well-known data sets and achieved positive results even with a very small number of teaching samples in the classroom. On the basis of the obtained results, it can be argued that the CNN deep learning model is an effective tool in the classification problems of the main sequence stars. The use of a large number of training samples increases computational accuracy regardless of the complexity of the waveform. The use of artificial neural networks shows its advantages precisely in processing tasks, classifications, identifying large data sets of complex signals, such as spectra of main sequence stars, etc.. Effective processing of large data sets, using a smaller resource base in a short processing time, is an urgent task of modern research, including the processing of astrophysical signals.

Stellar, planetary, and galactic studiesAstronomical Observations and InstrumentationAdvanced Neural Network Applicationsmain-sequence starsnormal starsconvolutional neural networks

More by S.a. Sarmanbetov