Parametric Adaptation as an Element of Mathematical Models Qualimetry of Complex Processes

Authors

  • B. L. Butvin Central Research Institute of the Armed Forces of Ukraine, Kyiv, Ukraine
  • O. O. Mashkin Central Research Institute of the Armed Forces of Ukraine, Kyiv, Ukraine
  • O. M. Sobolev Central Research Institute of the Armed Forces of Ukraine, Kyiv, Ukraine
  • V. E. Mykhalevych Central Research Institute of the Armed Forces of Ukraine, Kyiv, Ukraine
  • P.V. Open'ko Institute of Aviation and Air Defense, National Defense University of Ukraine named after Ivan Cherniakhovskyi, Kyiv, Ukraine
  • S. V. Maslenko Central Research Institute of the Armed Forces of Ukraine, Kyiv, Ukraine

DOI:

https://doi.org/10.3849/aimt.01541

Keywords:

analytical and simulation models, complex process, parametric adaptation of analytical models, qualimetry of models

Abstract

A possible approach to ensuring the necessary qualitative properties of analytical models by adapting their parameters to probable changes in the course of complex processes is considered. The approach involves the use of a polymodel description of processes with the aim of mutual compensation of the objective shortcomings of heterogeneous models, as well as the use of simulation modeling capabilities to adjust the parameters of analytical models in cases where the use of the latter is due to strict limitations on the time of obtaining calculation results and developing control influences based on them. The considered example of parametric adaptation of the Lanchester-type model reflects probable changes in the number of opposing sides during the conduct of hostilities.

Author Biography

  • P.V. Open'ko, Institute of Aviation and Air Defense, National Defense University of Ukraine named after Ivan Cherniakhovskyi, Kyiv, Ukraine

    канд. техн. наук

References

MIKONI, S.V., B.V. SOKOLOV and R.M. YUSUPOV. Qualimetry of Models and Polymodel Complexes (in Russian). St. Petersburg: Nauka, 2018. DOI 10.31857/S9785907036321000001.

RUMYANTSEV, M.I. On the Problem of the Adequacy Estimation of Simulation Models of the Banking Business Processes. In: Conference Proceedings SWorld. Odessa, 2010, 15, pp. 84-93. ISBN 978-966-555-152-2.

SMITH, J.S., D.T. STURROCK and D.W. KELTON. Simio and Simulation: Modeling, Analysis, Applications. 4th ed. Scotts Valley: CreateSpace, 2017. ISBN 1-54-646192-0.

LAW, A.M. Simulation Modeling and Analysis. 5th ed. New York: McGraw-Hill Education, 2015. ISBN 978-0-07-340132-4.

BALCI O. Validation, Verification and Testing Techniques throughout the Life Cycle of a Simulation Study. Annals of Operation Research, 1994, 53, pp. 121-173. DOI 10.1007/BF02136828.

Joint Conflict and Tactical Simulation Capabilities Brief [online]. 2018 [viewed 2021-10-06]. Available from: https://csl.llnl.gov/sites/csl/files/JCATS-LLNL-Brochure-30May2018.pdf

JTLS-GO Executive Overview [online]. 2021 [viewed 2021-10-10]. Available from: https://www.rolands.com/jtls/j_vdds/executive_overview.pdf

ARTEMIEV, S.S. and T.A. AVERINA. Numerical Analysis of Systems of Ordi-nary and Stochastic Differential Equation. Berlin: De Gruyter, 1997. ISBN 978-90-6764-250-7.

DEBRABANT, K. and A. ROESSLER. Classification of Stochastic Runge–Kutta Methods for the Weak Approximation of Stochastic Differential Equations. Mathematics and Computers in Simulation, 2008, 77(4), pp. 408-420. DOI 10.1016/j.matcom.2007.04.016.

Downloads

Published

24-11-2022

Issue

Section

Research Paper

Categories

How to Cite

Butvin, B. L., Mashkin, O. O. ., Sobolev, O. M. ., Mykhalevych, V. E., Open'ko, P., & Maslenko, S. V. (2022). Parametric Adaptation as an Element of Mathematical Models Qualimetry of Complex Processes. Advances in Military Technology, 17(2), 427-437. https://doi.org/10.3849/aimt.01541

Similar Articles

41-50 of 165

You may also start an advanced similarity search for this article.