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Your search keyword '"Maximenko, Vladimir A."' showing total 17 results
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17 results on '"Maximenko, Vladimir A."'

3. Computation of the spectrum of spatial Lyapunov exponents for the spatially extended beam-plasma systems and electron-wave devices

Author

Hramov, Alexander E., Koronovskii, Alexey A., Maximenko, Vladimir A., and Moskalenko, Olga I.

Subjects
Physics - Plasma Physics, Nonlinear Sciences - Chaotic Dynamics
Abstract

The spectrum of Lyapunov exponents is powerful tool for the analysis of the complex system dynamics. In the general framework of nonlinear dynamical systems a number of the numerical technics have been developed to obtain the spectrum of Lyapunov exponents for the complex temporal behavior of the systems with a few degree of freedom. Unfortunately, these methods can not apply directly to analysis of complex spatio-temporal dynamics in plasma devices which are characterized by the infinite phase space, since they are the spatially extended active media. In the present paper we propose the method for the calculation of the spectrum of the spatial Lyapunov exponents (SLEs) for the spatially extended beam-plasma systems. The calculation technique is applied to the analysis of chaotic spatio-temporal oscillations in three different beam-plasma model: (1) simple plasma Pierce diode, (2) coupled Pierce diodes, and (3) electron-wave system with backward electromagnetic wave. We find an excellent agreement between the system dynamics and the behavior of the spectrum of the spatial Lyapunov exponents. Along with the proposed method, the possible problems of SLEs calculation are also discussed. It is shown that for the wide class of the spatially extended systems the set of quantities included in the system state for SLEs calculation can be reduced using the appropriate feature of the plasma systems., Comment: 10 pages, 7 figures

Published
2013
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8. Efficiency and fatigue in marksmanship training

Author

Grubov, Vadim, primary, Badarin, Artem, additional, Savosenkov, Andrey, additional, Grigoriev, Nikita, additional, Lukojanov, Maxim, additional, Ydoratina, Anna, additional, Maximenko, Vladimir, additional, Kazantsev, Viktor B., additional, and Hramov, Alexander E., additional

Published
2021
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17. Forecasting macroscopic dynamics in adaptive Kuramoto network using reservoir computing.

Author

Andreev AV, Badarin AA, Maximenko VA, and Hramov AE

Subjects
Algorithms, Forecasting, Electroencephalography, Neural Networks, Computer, Machine Learning
Abstract

Forecasting a system's behavior is an essential task encountering the complex systems theory. Machine learning offers supervised algorithms, e.g., recurrent neural networks and reservoir computers that predict the behavior of model systems whose states consist of multidimensional time series. In real life, we often have limited information about the behavior of complex systems. The brightest example is the brain neural network described by the electroencephalogram. Forecasting the behavior of these systems is a more challenging task but provides a potential for real-life application. Here, we trained reservoir computer to predict the macroscopic signal produced by the network of phase oscillators. The Lyapunov analysis revealed the chaotic nature of the signal and reservoir computer failed to forecast it. Augmenting the feature space using Takkens' theorem improved the quality of forecasting. RC achieved the best prediction score when the number of signals coincided with the embedding dimension estimated via the nearest false neighbors method. We found that short-time prediction required a large number of features, while long-time prediction utilizes a limited number of features. These results refer to the bias-variance trade-off, an important concept in machine learning.

Published
2022
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