Fizika Nizkikh Temperatur: Volume 48, Number 6 (June 2022), p. 511-517    ( to contents , go back )

Noise signal as input data in self-organized neural networks

V. Kagalovsky1,2, D. Nemirovsky1, and S. V. Kravchenko3

1Shamoon College of Engineering, Beer-Sheva 84105, Israel

2Max-Planck-Institut für Physik komplexer Systeme, Dresden 01187, Germany

3Physics Department, Northeastern University, Boston, Massachusetts 02115, USA
E-mail: victork@sce.ac.il
pos Анотація:676

Received January 27, 2022, published online April 25, 2022

Abstract

Self-organizing neural networks are used to analyze uncorrelated white noises of different distribution types (normal, triangular, and uniform). The artificially generated noises are analyzed by clustering the measured time signal sequence samples without its preprocessing. Using this approach, we analyze, for the first time, the current noise produced by a sliding “Wigner-crystal”-like structure in the insulating phase of a 2D electron system in silicon. The possibilities of using the method for analyzing and comparing experimental data, obtained by observing various effects in solid-state physics, and numerical data, simulated using theoretical models are discussed.

Key words: self-organizing neural networks, current noise, Wigner crystal, 2D electron systems.

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