Ziyautdinov Vladimir Sergeevich (Candidate of Pedagogical Sciences, Associate Professor, University of Technology and Management named after K.G. Razumovsky (PKU) Lipetsk, Cossack Institute of Technology and Management (branch))
Zolotareva Tatiana Aleksandrovna (Senior Lecturer, Lipetsk State Pedagogical, University, them. P.P. Semenov-Tyan-Shansky)
Smirnov Mikhail Yuryevich (Candidate of Physical and Mathematical Sciences, Associate Professor, University of Technology and Management named after K.G. Razumovsky (PKU) Lipetsk Cossack Institute of Technology and Management (branch))
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In the modern world, it is increasingly necessary to deal with large amounts of information that are included in the concept of Big Data. When processing such information, it is necessary to clearly determine whether there is a relationship between various randomly selected blocks of data. In this paper, we consider various statistical criteria that test the data independence hypothesis and compare them. The application of these criteria allows you to determine whether there is a relationship between different information within big data. In addition, the article tested and analyzed the statistical hypothesis about the independence of data analysis by various numbers of artificial neurons. Subsequently, this will allow creating an optimal self-learning neural network to determine the independence of data when analyzing large amounts of information based on sample data.
Keywords:neuron, statistical criterion, data independence hypothesis.
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Citation link:
Ziyautdinov V. S., Zolotareva T. A., Smirnov M. Y. Strengthening mechanisms for testing the data independence hypothesis // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2022. -№08. -С. 81-87 DOI 10.37882/2223-2966.2022.08.18 |
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