Vol 78, No 3 (2017)

The problem of identification of the sinusoidal signal parameters is very popular in the modern theory and practice of the automatic control. However, the problem of quality of estimation related with the increase in the rate of parametric identification is studied weakly until now. At the same time, the requirements on the quality of processes at realization of the up-to-date systems represent one of the key criteria for selecting one or another approach. A new method for estimation of the sinusoidal signal parameters enabling an increase in the rate of parametric identification was proposed. Consideration was given to a signal representing a sum of two sinusoids which can be easily extended to a more general case.

For the space transport systems with a long uptime, consideration was given to the method of adaptive filtering in the problem of restoring the parameters of cosmic radiation flows from the measurement data. Proposed were a mathematical model and an algorithm for optimization of the nonstationary control systems whose state is measured against the noisy background. The algorithms of parametric optimization were based on a modified Wiener–Hopf equation and sensitivity functions.

We consider a new load balancing model that arises in the processing of user requests for files located on a given set of servers. The optimization criterion is the total excess of actual load over the limit load. In order to redistribute the load and minimize the criterion, files can be moved between the servers. We show that if there are no other constraints related to the stage of moving the files, then this problem is equivalent to a problem previously considered in literature. For this special case of this problem, we propose a stochastic local search scheme that combines a special procedure for fast querying of the neighborhoods and a procedure of non-aggravating modification of intermediate solutions. Results of numerical experiments show that the proposed approach is able to find high-quality solutions for instances of large dimension under tight time constraints.

Consideration was given to a model of recursive conveyor process which is an extension of the classical conveyor. A method for efficient calculation of the process time characteristics in a linear time was described. On the basis of the calculated characteristics, the scheduling of recursive conveyor processes comes to calculation of analytical expressions. The proposed model can be used in the APS-systems and MES-systems of control of production, as well as for scheduling in some traditional fields of application of the scheduling theory.

The sub-band analysis enabling one to construct a sequence whose Fourier transform is the best approximation of a segment of Fourier transform of the original series within a given frequency interval was shown to be an efficient tool to specify the trends of segments of the nonstationary time series. Relations were established defining the matrix operator to sort out such components. A procedure for adaptive construction of the operators for trend extraction was proposed, and conditions were determined under which a wide class of sequence segments are their eigenfunctions (fixed points) corresponding to the unit eigenvalues.

The networks of threshold elements are considered. The notion of a stationary ensemble is introduced. We set out necessary and sufficient conditions for a network to be an ensemble. We show that for two ensembles with common elements switching on one ensemble does not necessarily lead to switching on the other. A representation of an ensemble as a finite state machine is proposed. We then show how this representation can help study the processes of switching ensembles on and off. We note that a network of ensembles may be interpreted in neurobiological terms as a basis for a long-term memory model. In social sciences it may serve as a network version of the collective social threshold behavior model.

Modern problems of optimization, estimation, signal and image processing, pattern recognition, etc., deal with huge-dimensional data; this necessitates elaboration of efficient methods of processing such data. The idea of building low-dimensional approximations to huge data arrays is in the heart of the modern data analysis.

One of the most appealing methods of compact data representation is the statistical method referred to as the principal component analysis; however, it is sensitive to uncertainties in the available data and to the presence of outliers. In this paper, robust versions of the principle component analysis approach are proposed along with numerical methods for their implementation.

Consideration was given to the problem of constructing the lower confidence limit for the probability of failsafe system operation (reliability function) from the results of testing its elements. Solution of this problem was obtained for a rather general case of system elements ageing with monotone increasing function of failure rate. In case where the system can be redundantized both by identical and different-type elements, approximate asymptotic expressions were established for the case of high reliability.

This paper introduces a calculation method for the minimum admissible octane number subject to the measurement errors, which is the objective in the gasoline blending optimization problems. The tasks and functionality of the on-line blending optimization system are formulated. A periodic calibration validation algorithm is suggested for the in-flow blend quality analyzers as the key elements of the optimization system. Finally, some pilot projects on the implementation of the optimization systems with in-flow blend quality analysis are presented.

This paper considers a monopolistic competition model with the endogenous choice of technology in the closed economy case. Our aim is to obtain the comparative statics of the equilibrium and socially optimal solutions with respect to the technological innovation parameter that affects costs. The key findings are the following: consumption and investments in productivity both increase with the growth of technological innovation; the behavior of the equilibrium variables depends on the elasticity of demand only; the behavior of the socially optimal variables depends on the elasticity of utility only; finally, the behavior of the equilibrium and socially optimal variables does not depend on the properties of the costs as a function of investments in R&D.