Vyacheslav F. Gubarev

Method of Iterative Identification of Multidimensional Systems by Uncertain Data. Part I. Theoretical Aspects

we suggested a model of competitiveness of brand product, which takes into account 10 factors. The model is based on 52 fuzzy rules of type. Potential of application of the model for control of competitiveness of brand product is shown. We state a problem of learning of fuzzy model of competitiveness on the basis of experimental data.

On Peculiarities of Identification of Multidimensional Continuous Systems by Data with Bounded Indeterminacy

we suggested a model of competitiveness of brand product, which takes into account 10 factors. The model is based on 52 fuzzy rules of type. Potential of application of the model for control of competitiveness of brand product is shown. We state a problem of learning of fuzzy model of competitiveness on the basis of experimental data.

THE GENERALIZED LEAST-SQUARES SYSTEM IDENTIFICATION SUBJECT TO UNKNOWN BOUNDED NOISE

Abstract On the base of the least-squares technique the universal method of regularized identification is developed. For any given input and output in the presence of the worst-case bounded noise it provides the convergent parameter estimate when the order of stable model is increased. This implies that even in the case when available information is not sufficient to identify the system it is possible to evaluate an approximate model using the approach offered in the paper. The recurrent form of regularized least-squares algorithm with adaptive regularization depending on incoming data is constructed.

State Estimation for Systems Subjected to Bounded Uncertainty Using Moving Horizon Approach

Abstract This paper deals with the moving horizon recursive state estimation for nonlinear continuous and discrete time systems in assumption that perturbations and measurement noise belong to a bounded set but without any stochastic information. Moving finite time horizon approach allows to apply variation principle and residual equations in order to get the state estimation compatible with errors and assimilate the data in model. Equations of observer were derived on the base of Euler equations for variation problem. Results of numerical simulation are given to justify the stability and performance of the proposed filter.

RATIONAL APPROXIMATION AND IDENTIFICATION OF DISTRIBUTED PARAMETER SYSTEMS

The unified infinite dimensional model structure which assumes on its base to develop the method and algorithms of systems rational approximation and identification is proposed for distributed parameter systems with discrete inputs and outputs. The considered truncated realization converges to infinite-dimensional non-rational model of system for nuclear type operators. Approximation is represented by series expansion on independent basis functions which are fundamental solutions of ordinary differential equations. The using of Jordan realization have succeeded in creation of iterative identification algorithm admitting sequential model reconstruction by separate parts consisting of one or several modes.

Modified Regularization in System State Estimation and Parameter Identification for State Space Models

Abstract Regularization as standard procedure dealing with ill-posed problems has gained acceptance in system identification. State estimation problem for given model with disturbances and noise is considered at first. Simple relation between regularization coefficient and the rough properties of the perturbations in system is obtained. As a result the simple-to-tune filter can be constructed on the base of derived equations. The introduced approach was then developed to the case of state and parameter joint estimation including system identification and to more complicated case of model structure selection. Using more sophisticated regularization for the overparametrized model the approximate solution with minimal norm on the observation interval can be found for the extended estimation problem. Numerical experiments were held to illustrate the filters and criterions properties.

Control of plasma in tokamak equipment

This article examines the attempts to develop high-power, fast-acting controllable electromagnetic systems for the control of plasma in tokamaks. Work has been completed on a controllable thyristor current regulator for the windings that shape the transverse magnetic field of tokamaks. Toroidal equipment is being developed to check experimentally various control effects on a plasma, to investigate the optimal modes of development for the discharge, and to work out stabilizing systems for the parameters of the plasmas state in order to improve the efficiency of the physical studies. Topics considered include the control of the magnetic configuration, the control of the evolution of distributions for plasma parameters, and the control of magnetohydrodynamic (MHD) fluctuations in a plasma.