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