ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)


Nonlinear dynamical systems

Development of the Russian state in the 20th and 21st centuries: Mathematical modeling based on the socio-energy approach

Purpose. The article is devoted to modeling the socio-political development of Russia in 1910–2009 based on the author’s socio-energy approach. In this paper, we briefly talk about the basics of the proposed approach, its principles and basic equations. Methods. The mathematical model is based on the Langevin diffusion equation. We also introduce the concepts of social energy, coefficients of the state of society and give them definitions. Results.

Development of the Russian state in the 20th and 21st centuries: mathematical modeling based on the socio-energy approach

Purpose. The article is devoted to modeling the socio-political development of Russia in 1910-2009 based on the author's socio-energy approach. In this paper, we briefly talk about the basics of the proposed approach, its principles and basic equations.
Methods. The mathematical model is based on the Langevin diffusion equation. We also introduce the concepts of social energy, coefficients of the state of society and give them definitions.

Influence of the choice of the model structure for working capacity of nonlinear Granger causality approach

Currently, the method of nonlinear Granger causality is actively used in many applications in medicine, biology, physics, to identify the coupling between objects from the records of their oscillations (time series) using forecasting models. In this paper the impact of choosing the model structure on the method performance is investigated. The possibility of obtaining reliable estimates of coupling is numerically demonstrated, even if the structure of the constructed forecasting model differs from that of the reference system

Modelling ensembles of nonlinear continuous time dynamical systems in active ultra wideband wireless networks

The paper deals with a new multi­element processor platform to model the behavior of interacting dynamical systems – active wireless network. Each dynamical system modeling process, is associated with an active network node. The interaction between the dynamical systems is made through the transfer of information on the state of the system through radio channels between nodes of active network. Platform capabilities are demonstrated by an ensemble of oscillators Kuramoto. Describes the technique of modeling, experimental results and their analysis.