Methodology for assessing the adequacy of statistical simulation models
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematika, mehanika, fizika, Tome 15 (2023) no. 3, pp. 5-14 Cet article a éte moissonné depuis la source Math-Net.Ru

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Statistical simulation models of complex technical systems characterized by several indicators of operational efficiency were studied in this paper. The efficiency of obtaining knowledge on the examined systems depends on the quality of the models used. One of the basic properties describing the quality of a model is its adequacy – the complex property characterizing the degree of conformity of the values of the output parameters of the model with the object with the required accuracy and reliability. Current approaches to evaluating the adequacy of models are based on various subjective convolutions of confidence factors from research results to a generalized indicator the essence of which, as a rule, is not interpreted. The presented method of assessing the adequacy of statistical simulation models of complex technical systems with several performance indicators differs from existing methods by using a generalized indicator of adequacy, which is the probability of achieving the required confidence of all the accuracy requirements to determine each of the considered performance indicators. This indicator is a natural, unambiguously interpreted (the probability of satisfying the requirements for model adequacy) objective and generalized indicator of adequacy of the simulation model. For preliminary calculations we use the Parzen–Rosenblatt method and obtain the probability density function of distances between real and model indicators of effectiveness of the examined system. The required result is then obtained by the suggested algorithm of multiple integration of the density function using the Monte-Carlo method. Recommendations on the realization of the computational procedures foreseen by the method are given. The application of the method is illustrated by a description of a computational experiment.
Keywords: simulation modeling, accuracy, reliability, model adequacy, complex technical system.
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R. M. Vivchar; A. I. Ptushkin; B. V. Sokolov. Methodology for assessing the adequacy of statistical simulation models. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematika, mehanika, fizika, Tome 15 (2023) no. 3, pp. 5-14. http://geodesic.mathdoc.fr/item/VYURM_2023_15_3_a0/

[1] Mikoni S.V., Sokolov B.V., Yusupov R.M., Qualimetry of Models and Polymodel Complexes, Monograph, RAN Publ, M., 2018, 314 pp. (in Russ.) | DOI

[2] Vivchar R.M., Ptushkin A.I., Sokolov B.V., “Risk-Based Management of the Design of Organisational and Technical Systems Based on Simulation Models of their Functioning”, Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies, 2021, no. 2, 17–31 (in Riss.) | DOI

[3] Stepenko A.N., Reshetnikov D.V., Andreev E.A., Levchuk A.A., “Model of the System of Operation of Power Supply Systems of a High-Risk Object”, Modern high technologies, 2021, no. 11-2, 289-293 (in Russ.) | DOI

[4] Rostovtsev Yu.G., Yusupov R.M., “The Problem of Ensuring the Adequacy of Subject-Object Modeling”, Izvestiya vuzov. Priborostroenie, 1991, no. 7, 7–14 (in Russ.)

[5] Venttsel E.S., Probability Theory, Nauka Publ, M., 1969, 576 pp. (in Russ.)

[6] Smirnov N.V., Dunin-Barskovskiy I.V., A Short Course in Mathematical Statistics for Technical Applications, Fizmatgiz Publ, M., 1959, 436 pp. (in Russ.)

[7] Vivchar R.M., Ptushkin A.I., Sokolov B.V., “The Technique for Multi-Criteria Evaluation of the Performance of Stochastic Complex Technical Systems”, Aerospace Instrument-Making, 2022, no. 7, 3–14 | DOI

[8] Davydov V.S., “Recognition of Incipient Defects in the Units of Ship Machinery by Vibrodiagnostics Based on Optimum Decision Rules”, Russian Journal of Nondestructive Testing, 55: 3 (2019), 185–191 | DOI

[9] Porshnev S.V., Koposov A.S., “Using Rozenblatt-Parzen Approximaion for Recovering a Cumulative Distribution Function of Continuous Random Variable with a Bounded Single-Mode Distribution Rule”, Scientific Journal of KubSAU, 2013, no. 92(08), 1–27

[10] E. Parzen, “Parzen, E. On Estimation of a Probability Density Function and Mode”, Ann. Math. Statist., 33:3 (1962), 1065–1076 | DOI | MR | Zbl

[11] Markovich L.A., “Nonparametric Estimation of Multivariate Density and its Derivative by Dependent Data using Gamma Kernels”, Fundamentalnaya i prikladnaya matematika, 22:3 (2018), 145–177

[12] Parmuzina M.S., Modebeykin A.A., Sukhanov A.A., “Calculation of Integrals by the Monte Carlo method”, E-SCIO, 2022, no. 6(69), 553–565