


Vol 79, No 8 (2018)
- Year: 2018
- Articles: 12
- URL: https://ogarev-online.ru/0005-1179/issue/view/9028
Linear Systems
Frequency-Domain Stability Conditions for Discrete-Time Switched Systems
Abstract
We consider discrete-time switched systems with switching of linear time-invariant right-hand parts. The notion of a connected discrete switched system is introduced. For systems with the connectedness property, we propose necessary and sufficient frequency-domain conditions for the existence of a common quadratic Lyapunov function that provides the stability for a system under arbitrary switching. The set of connected switched systems contains discrete control systems with several time-varying nonlinearities from the finite sectors, considered in the theory of absolute stability. We consider the case of switching between three linear subsystems in more details and give an illustrative example.



Nonlinear Systems
Localization of Invariant Compacts in Multidimensional Systems with Phase Control
Abstract
We consider phase systems of order six, four, and three that admit chaotic attractors of various types. We apply a localization method that makes it possible to find regions in the phase space (localizing sets) that contain all attractors of the system. We obtain systems of inequalities that define localizing sets and represent estimates of the amplitudes of established oscillations and chaotic attractors.



Stochastic Systems
Gradient-Free Two-Point Methods for Solving Stochastic Nonsmooth Convex Optimization Problems with Small Non-Random Noises
Abstract
We study nonsmooth convex stochastic optimization problems with a two-point zero-order oracle, i.e., at each iteration one can observe the values of the function’s realization at two selected points. These problems are first smoothed out with the well-known technique of double smoothing (B.T. Polyak) and then solved with the stochastic mirror descent method. We obtain conditions for the permissible noise level of a nonrandom nature exhibited in the computation of the function’s realization for which the estimate on the method’s rate of convergence is preserved.



M-Estimates of Autoregression with Random Coefficients
Abstract
Asymptotic normality of the M-estimates of the autoregression parameters of the autoregression equation with random coefficients was proved. A method to calculate the asymptotic relative efficiency of the M-estimate with ρ-function relative to the least squares estimate was presented for the first-order equation. The method is based on the expansion of the asymptotic variance of the M-estimate into a converging series. The M-estimate was shown to be superior to the least-squares estimate if the regenerative process has a contaminated Gaussian distribution.



Planning the Resource of Information Transmission for Connection Lines of Multiservice Hierarchical Access Networks
Abstract
We construct and investigate a mathematical model for joint servicing of real-time multiservice traffic in access networks with a hierarchical topology. The model takes into account how the arrival of claims in each flow depends on the number of users that form this flow of claims and that are being serviced. We define quality indicators for joint servicing of claims through the values of stationary probabilities in the model. We obtain relations between them that simplify their estimation. We develop an algorithm for calculating the characteristics of the model based on an implementation of the convolution algorithm. We propose a procedure for estimating the transmission rate of connecting lines in the access network needed to service incoming traffic with a given quality. We also give numerical examples that illustrate the characteristic features of implementing the developed computational procedures.



Terminal Invariance of Stochastic Diffusion Systems
Abstract
For a controlled stochastic diffusion system, we obtain sufficient conditions for the terminal criterion to be constant with probability one on the assumption of fixed initial state (invariance in perturbations) and sufficient conditions for the terminal criterion to be independent with probability one both from the realization of the random process and the initial conditions (absolute invariance).



Intellectual Control Systems, Data Analysis
Statistical Control of Defects in a Continuously Cast Billet Based on Machine Learning and Data Analysis Methods
Abstract
We consider the problems of defects arising in the production of continuously cast billets at continuous casting plants. We propose a model for predicting slab cracks based on the random forest machine learning algorithm. We determine the main technological parameters that influence the appearance of cracks and present the results of the model.



Meixner Nonorthogonal Filters
Abstract
Consideration was given to a new representation of the Meixner filters which, in distinction to the previously proposed filters, have a rational form of representation of any integer values of the additional parameter α, can be used to describe the dynamic systems with fractional order for the noninteger α, and are obtained directly from the continuous generalized Laguerre filters through a modified bilinear transformation. The paper described a design of the proposed nonorthogonal Meixner filter, numerically stable algorithm to optimize the filter parameters, as well as the results of computer experiments corroborating efficiency of the nonorthogonal Meixner filters for solution of practical problems.



Optimization, System Analysis, and Operations Research
Deep Learning Model Selection of Suboptimal Complexity
Abstract
We consider the problem of model selection for deep learning models of suboptimal complexity. The complexity of a model is understood as the minimum description length of the combination of the sample and the classification or regression model. Suboptimal complexity is understood as an approximate estimate of the minimum description length, obtained with Bayesian inference and variational methods. We introduce probabilistic assumptions about the distribution of parameters. Based on Bayesian inference, we propose the likelihood function of the model. To obtain an estimate for the likelihood, we apply variational methods with gradient optimization algorithms. We perform a computational experiment on several samples.



Mathematical Game Theory and Applications
Positional Voting Methods Satisfying the Criteria of Weak Mutual Majority and Condorcet Loser
Abstract
This paper considers a voting problem in which the individual preferences of electors are defined by the ranked lists of candidates. For single-winner elections, we apply the criterion of weak positional dominance (WPD, PD), which is closely related to the positional scoring rules. Also we formulate the criterion of weak mutual majority (WMM), which is stronger than the majority criterion but weaker than the criterion of mutual majority (MM). Then we construct two modifications for the median voting rule that satisfy the Condorcet loser criterion. As shown below, WPD and WMM are satisfied for the first modification while PD and MM for the second modification. We prove that there is no rule satisfying WPD and MM simultaneously. Finally, we check a list of 37 criteria for the constructed rules.



Strongly Subgame-Consistent Core in Stochastic Games
Abstract
This paper investigates stochastic games on finite tree graphs. A given n-player normal-form game is defined at each node of a tree. Transition to a next node of the tree is random and depends on the strategy profile realized in a current game. We construct a cooperative solution of the game by maximizing the total expected payoff of the players. The core is used as the solution concept of the cooperative game. We introduce the definition of a strongly subgame-consistent (strongly time-consistent) core. Finally, we suggest a method for designing a cooperative distribution procedure of an imputation from the core that guarantees its strong subgame consistency.



On a Modification of the Multistage Bidding Model with Continuous Bids and Asymmetric Information
Abstract
This paper considers a modification of the multistage bidding model with continuous bids. Bidding takes place between two players for one unit of a risky asset (one stock). Player 1 knows the real price of the asset while Player 2 knows only the probabilities of high and low prices of the asset. At each stage of the bidding, players make real valued bids. The higher bid wins, and one unit of the risky asset is transacted to the winning player. The price of the transaction is a convex combination of the bids with a given coefficient. The optimal strategies of the players and the value of the n-stage game are found.


