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Method and algorithms of nonlinear dynamic processes identification

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Category: Information technologies, systems analysis and administration

Last Updated on 02 April 2016

Published on 02 April 2016

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Authors:

V.I. Korniіenko, Dr. Sci. (Tech.), State Higher Educational Institution “National Mining University”, Professor of Security Information and Telecommunications Department, Dnipropetrovsk, Ukraine.

S.M. Matsiuk, State Higher Educational Institution “National Mining University”, Doctoral Student of the Department of Computer Systems Software, Dnipropetrovsk, Ukraine.

I.M. Udovyk, Cand. Sci. (Tech.), State Higher Educational Institution “National Mining University”, Docent of the Department of Computer Systems Software, Dnipropetrovsk, Ukraine.

О.M. Alekseіev, Cand. Sci. (Tech.), State Higher Educational Institution “National Mining University”, Senior Lecturer of the Department of System Analysis and Control, Dnipropetrovsk, Ukraine.

Abstract:

Purpose. Increasing accuracy of dynamic models of the complex nonlinear processes for solution of the tasks for these processes control.

Methodology. Structural-parametric identification of nonlinear dynamic processes including the identification of the mo-del structure based on selection by non-shift criterion, as well as self-reactance identification of the optimum structure model by the regularity criterion through the whole sampling of experimental data.

Findings. The algorithms of global and local optimization of nonlinear dynamic process models realizing the procedure of structural-parametric identification by their structural and self-reactance optimization were developed, which allows getting the models of extra accuracy.

Originality. The method of identification of nonlinear dynamic processes consisting of procedures for estimation of the state and characteristics of the process, as well as their structural-parametric identification, was offered. It allows, unlike the known of methods, to fulfil the identification of these processes in the batch mode by structural-parametric and in real-time mode by self-reactance optimization of their models.

Practical value. The results of the research can be used while developing algorithms for controlling complex nonlinear processes based on their complex estimation and identification.

References:

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