USE OF INFORMATION UNCERTAINTY IN IDENTIFICATION TASKS

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USE OF INFORMATION UNCERTAINTY IN IDENTIFICATION TASKS

Journal Title: Комп’ютерне моделювання: аналіз, управління, оптимізація - Year 2019, Vol 1, Issue 1

Abstract

Informational methods for analyzing and managing systems under uncertainty are studied. The expediency of use of information uncertainty in tasks of identification of control objects and synthesis of regulatory systems is justified. For the numerical evaluation of information uncertainty, an amount of disinformation is used as Bongard’s negative useful information. Such information uncertainty can serve as a criterion for the adequacy of a mathematical model of a control object. Information assessment of the modeling accuracy is applicable to any method of identification and allows a researcher to compare the accuracy of models that differ from each other by the method of obtaining (analytical or experimental), nature (deterministic or stochastic models), specific implementation (physical or mathematical models). In the course of parametric identification, an optimization problem of finding a minimum of information uncertainty in the parameter space of a mathematical model can be solved. The information uncertainty criterion provides verification of the statistical hypothesis about the adequacy of a particular model. If the criterion value exceeds a certain critical value, the adequacy hypothesis must be rejected. To calculate the critical values of the information adequacy criterion, a statistical experiment was performed. Using the Monte Carlo method, the probability distribution of the information criterion was investigated. A sufficiently smooth empirical criterion distribution function was constructed. The distribution of the information criterion has a pronounced asymmetry and a small positive kurtosis. It is revealed that this distribution is best approximated by the Generalized Extreme Value Distribution law. The critical value can be defined as a quantile of the level of 0.01 or 0.05 for this distribution.

Authors and Affiliations

G. I. Manko, E. P. Chistokletov

Keywords

adequacy computer modeling distribution law identification information criterion Monte Carlo method control object statistical hypothesis uncertainty.

EP ID EP642565
DOI 10.32434/2521-6406-2019-5-1-30-35
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