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This article presents the results of an analytical and computer study of the collective dynamic properties of a chain of self-oscillating systems (conditionally — oscillators). It is assumed that the couplings of individual elements of the chain are non-reciprocal, unidirectional. More precisely, it is assumed that each element of the chain is under the influence of the previous one, while the reverse reaction is absent (physically insignificant). This is the main feature of the chain. This system can be interpreted as an active discrete medium with unidirectional transfer, in particular, the transfer of a matter. Such chains can represent mathematical models of real systems having a lattice structure that occur in various fields of natural science and technology: physics, chemistry, biology, radio engineering, economics, etc. They can also represent models of technological and computational processes. Nonlinear self-oscillating systems (conditionally, oscillators) with a wide “spectrum” of potentially possible individual self-oscillations, from periodic to chaotic, were chosen as the “elements” of the lattice. This allows one to explore various dynamic modes of the chain from regular to chaotic, changing the parameters of the elements and not changing the nature of the elements themselves. The joint application of qualitative methods of the theory of dynamical systems and qualitative-numerical methods allows one to obtain a clear picture of all possible dynamic regimes of the chain. The conditions for the existence and stability of spatially-homogeneous dynamic regimes (deterministic and chaotic) of the chain are studied. The analytical results are illustrated by a numerical experiment. The dynamical regimes of the chain are studied under perturbations of parameters at its boundary. The possibility of controlling the dynamic regimes of the chain by turning on the necessary perturbation at the boundary is shown. Various cases of the dynamics of chains comprised of inhomogeneous (different in their parameters) elements are considered. The global chaotic synchronization (of all oscillators in the chain) is studied analytically and numerically.

This paper presents algorithm for modeling set of trajectories of non-stationary time series, based on a numerical scheme for approximating the sample density of the distribution function in a problem with fixed ends, when the initial distribution for a given number of steps transforms into a certain final distribution, so that at each step the semigroup property of solving the Liouville equation is satisfied. The model makes it possible to numerically construct evolving densities of distribution functions during random switching of states of the system generating the original time series.

The main problem is related to the fact that with the numerical implementation of the left-hand differential derivative in time, the solution becomes unstable, but such approach corresponds to the modeling of evolution. An integrative approach is used while choosing implicit stable schemes with “going into the future”, this does not match the semigroup property at each step. If, on the other hand, some real process is being modeled, in which goal-setting presumably takes place, then it is desirable to use schemes that generate a model of the transition process. Such model is used in the future in order to build a predictor of the disorder, which will allow you to determine exactly what state the process under study is going into, before the process really went into it. The model described in the article can be used as a tool for modeling real non-stationary time series.

Steps of the modeling scheme are described further. Fragments corresponding to certain states are selected from a given time series, for example, trends with specified slope angles and variances. Reference distributions of states are compiled from these fragments. Then the empirical distributions of the duration of the system’s stay in the specified states and the duration of the transition time from state to state are determined. In accordance with these empirical distributions, a probabilistic model of the disorder is constructed and the corresponding trajectories of the time series are modeled.

The stationary flame spread rate has been calculated using the relationship based on the thermodynamic variational principle. It has been shown that proposed numerical algorithm provides the stable convergence under any initial approximation, which could be noticeably far from the searched solution.

A classical for nonlinear dynamics model, Brusselator, is considered, being augmented by addition of a third variable, which plays the role of a fast-diffusing inhibitor. The model is investigated in one-dimensional case in the parametric domain, where two types of diffusive instabilities of system’s homogeneous stationary state are manifested: wave instability, which leads to spontaneous formation of autowaves, and Turing instability, which leads to spontaneous formation of stationary dissipative structures, or Turing structures. It is shown that, due to the subcritical nature of Turing bifurcation, the interaction of two instabilities in this system results in spontaneous formation of stationary dissipative structures already before the passage of Turing bifurcation. In response to different perturbations of spatially uniform stationary state, different stable regimes are manifested in the vicinity of the double bifurcation point in the parametric region under study: both pure regimes, which consist of either stationary or autowave dissipative structures; and mixed regimes, in which different modes dominate in different areas of the computational space. In the considered region of the parametric space, the system is multistable and exhibits high sensitivity to initial noise conditions, which leads to blurring of the boundaries between qualitatively different regimes in the parametric region. At that, even in the area of dominance of mixed modes with prevalence of Turing structures, the establishment of a pure autowave regime has significant probability. In the case of stable mixed regimes, a sufficiently strong local perturbation in the area of the computational space, where autowave mode is manifested, can initiate local formation of new stationary dissipative structures. Local perturbation of the stationary homogeneous state in the parametric region under investidation leads to a qualitatively similar map of established modes, the zone of dominance of pure autowave regimes being expanded with the increase of local perturbation amplitude. In two-dimensional case, mixed regimes turn out to be only transient — upon the appearance of localized Turing structures under the influence of wave regime, they eventually occupy all available space.

Stochastically forced discrete dynamical systems are considered. Using first approximation systems, we study dynamics of deviations of stochastic solutions from deterministic equilibria. Necessary and sufficient conditions of the existence of stable stationary solutions of equations for mean-square deviations are derived. Stationary values of these mean-square deviations are used for the estimations of the dispersion of random states nearby stable equilibria and analysis of noise-induced transitions. Constructive application of the suggested technique to the analysis of various stochastic regimes in Ricker population model with Allee effect is demonstrated.

This article studies a method of constructing a predictive neural network model of a time series based on determining the composition of input variables, constructing a training sample and training itself using the back propagation method. Traditional methods of constructing predictive models of the time series are: the autoregressive model, the moving average model or the autoregressive model — the moving average allows us to approximate the time series by a linear dependence of the current value of the output variable on a number of its previous values. Such a limitation as linearity of dependence leads to significant errors in forecasting.

Mining Technologies using neural network modeling make it possible to approximate the time series by a nonlinear dependence. Moreover, the process of constructing of a neural network model (determining the composition of input variables, the number of layers and the number of neurons in the layers, choosing the activation functions of neurons, determining the optimal values of the neuron link weights) allows us to obtain a predictive model in the form of an analytical nonlinear dependence.

The determination of the composition of input variables of neural network models is one of the key points in the construction of neural network models in various application areas that affect its adequacy. The composition of the input variables is traditionally selected from some physical considerations or by the selection method. In this work it is proposed to use the behavior of the autocorrelation and private autocorrelation functions for the task of determining the composition of the input variables of the predictive neural network model of the time series.

In this work is proposed a method for determining the composition of input variables of neural network models for stationary and non-stationary time series, based on the construction and analysis of autocorrelation functions. Based on the proposed method in the Python programming environment are developed an algorithm and a program, determining the composition of the input variables of the predictive neural network model — the perceptron, as well as building the model itself. The proposed method was experimentally tested using the example of constructing a predictive neural network model of a time series that reflects energy consumption in different regions of the United States, openly published by PJM Interconnection LLC (PJM) — a regional network organization in the United States. This time series is non-stationary and is characterized by the presence of both a trend and seasonality. Prediction of the next values of the time series based on previous values and the constructed neural network model showed high approximation accuracy, which proves the effectiveness of the proposed method.

The work is devoted to the problem of creating a model with stationary parameters using historical data under conditions of unknown disturbances. The case is considered when a representative sample of object states can be formed using historical data accumulated only over a significant period of time. It is assumed that unknown disturbances can act in a wide frequency range and may have low-frequency and trend components. In such a situation, including data from different time periods in the sample can lead to inconsistencies and greatly reduce the accuracy of the model. The paper provides an overview of approaches and methods for data harmonization. In this case, the main attention is paid to data sampling. An assessment is made of the applicability of various data sampling options as a tool for reducing the level of uncertainty. We propose a method for identifying a self-leveling object model using data accumulated over a significant period of time under conditions of unknown disturbances with a wide frequency range. The method is focused on creating a model with stationary parameters that does not require periodic reconfiguration to new conditions. The method is based on the combined use of sampling and presentation of data from individual periods of time in the form of increments relative to the initial point in time for the period. This makes it possible to reduce the number of parameters that characterize unknown disturbances with a minimum of assumptions that limit the application of the method. As a result, the dimensionality of the search problem is reduced and the computational costs associated with setting up the model are minimized. It is possible to configure both linear and, in some cases, nonlinear models. The method was used to develop a model of closed cooling of steel on a unit for continuous hot-dip galvanizing of steel strip. The model can be used for predictive control of thermal processes and for selecting strip speed. It is shown that the method makes it possible to develop a model of thermal processes from a closed cooling section under conditions of unknown disturbances, including low-frequency components.

We study the influence of fishery on a structured fish population under random changes of habitat conditions. The population parameters correspond to dominant pelagic fish species of Far-Eastern seas of the northwestern part of the Pacific Ocean (pollack, herring, sardine). Similar species inhabit various parts of the Word Ocean. The species body size distribution was chosen as a main population feature. This characteristic is easy to measure and adequately defines main specimen qualities such as age, maturity and other morphological and physiological peculiarities. Environmental fluctuations have a great influence on the individuals in early stages of development and have little influence on the vital activity of mature individuals. The fishery revenue was chosen as an optimality criterion. The main control characteristic is fishing effort. We have chosen quadratic dependence of fishing revenue on the fishing effort according to accepted economic ideas stating that the expenses grow with the production volume. The model study shows that the population structure ensures the increased population stability. The growth and drop out of the individuals’ due to natural mortality smoothens the oscillations of population density arising from the strong influence of the fluctuations of environment on young individuals. The smoothing part is played by diffusion component of the growth processes. The fishery in its turn smooths the fluctuations (including random fluctuations) of the environment and has a substantial impact upon the abundance of fry and the subsequent population dynamics. The optimal time-dependent fishing effort strategy was compared to stationary fishing effort strategy. It is shown that in the case of quickly changing habitat conditions and stochastic dynamics of population replenishment there exists a stationary fishing effort having approximately the same efficiency as an optimal time-dependent fishing effort. This means that a constant or weakly varying fishing effort can be very efficient strategy in terms of revenue.

We study an appearance of gravitational convection in a porous medium saturated by the double-diffusive fluid. The rectangle heated from below is considered with anisotropy of media properties. We analyze Darcy – Boussinesq equations for a binary fluid with Soret effect.

Resulting system for the stream function, the deviation of temperature and concentration is cosymmetric under some additional conditions for the parameters of the problem. It means that the quiescent state (mechanical equilibrium) loses its stability and a continuous family of stationary regimes branches off. We derive explicit formulas for the critical values of the Rayleigh numbers both for temperature and concentration under these conditions of the cosymmetry. It allows to analyze monotonic instability of mechanical equilibrium, the results of corresponding computations are presented.

A finite-difference discretization of a second-order accuracy is developed with preserving of the cosymmetry of the underlying system. The derived numerical scheme is applied to analyze the stability of mechanical equilibrium.

The appearance of stationary and nonstationary convective regimes is studied. The neutral stability curves for the mechanical equilibrium are presented. The map for the plane of the Rayleigh numbers (temperature and concentration) are displayed. The impact of the parameters of thermal diffusion on the Rayleigh concentration number is established, at which the oscillating instability precedes the monotonic instability. In the general situation, when the conditions of cosymmetry are not satisfied, the derived formulas of the critical Rayleigh numbers can be used to estimate the thresholds for the convection onset.

Numerical study of unsteady mixed convection in an open partially porous horizontal channel with a heatgenerating source was performed. The outer surfaces of horizontal walls of finite thickness were adiabatic. In the channel there was a Newtonian heat-conducting fluid with a temperature-dependent viscosity. The discrete heatconducting and heat-generating source is located inside the bottom wall. The temperature of the fluid phase was equal to the temperature of the porous medium, and calculations were performed using the local thermal equilibrium model. The porous insertion is isotropic, homogeneous and permeable to fluid. The Darcy–Brinkman model was used to simulate the transport process within the porous medium. Governing equations formulated in dimensionless variables “stream function – vorticity – temperature” using the Boussinesq approximation were solved numerically by the finite difference method. The vorticity dispersion equation and energy equation were solved using locally one-dimensional Samarskii scheme. The diffusive terms were approximated by central differences, while the convective terms were approximated using monotonic Samarskii scheme. The difference equations were solved by the Thomas algorithm. The approximated Poisson equation for the stream function was solved separately by successive over-relaxation method. Optimal value of the relaxation parameter was found on the basis of computational experiments. The developed computational code was tested using a set of uniform grids and verified by comparing the results obtained of other authors.

Numerical analysis of unsteady mixed convection of variable viscosity fluid in the horizontal channel with a heat-generating source was performed for the following parameters: $\mathrm{Pr} = 7.0$, $\varepsilon = 0.8$, $\mathrm{Gr} = 10^5$, $C = 0-1$, $10^{-5} < \mathrm{Da} < 10^{-1}$, $50 < \mathrm{Re} < 500$, $\delta = l/H = 0.6-3$. Distributions of the isolines of the stream function, temperature and the dependences of the average Nusselt number and the average temperature inside the heater were obtained in a steady-state regime, when the stationary picture of the flow and heat transfer is observed. As a result we showed that an addition of a porous insertion leads to an intensification of heat removal from the surface of the energy source. The increase in the porous insertion sizes and the use of working fluid with different thermal characteristics, lead to a decrease in temperature inside the source.