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  8. 41-3

Electronic modeling

Vol 41, No 3 (2019)

 

CONTENTS

Mathematical Modeling and Computation Methods

  SAUKH S.Ye., BORISENKO A.V.
Method of Forecasting the Dynamics of Monthly Electricity Production by NPP Units of Ukraine
3-14
  KRASILNIKOV A.I.
Family of Subbotin Distributions and its Classification
15-32
  IVANIUK V.A., FEDORCHUK V.A.
Adaptive Method of Identification of Models of Nonlinear Dynamic Systems with Using Integral Volterra Series
33-42
  TKACHUK V.M., KOZLENKO M.I., KUZ M.V., LAZAROVYCH I.M., DUTCHAK M.C. Function
Optimization Based on Higher-Order Quantum Genetic Algorithm
43-58

Parallel Computations

  HILGURT S.Ya.
Analysis: Fpga-Based Cams and Digital Comparators for Pattern Matching in Network Security
59-80

Application of Modeling Methods and Facilities

  KUTSAN Yu.G., GODUN O.V., KYRIANCHUK V.N.
Sensitivity Analysis for Comparative Evaluation of Ukrainian Nuclear Fuel Cycle
81-92
  FARHADZADEH E.M., MURADALIYEV A.Z., RAFIYEVA T.K., RUSTAMOVA A.A.
Computer Technology of the Estimation of Interrelation Technical and Economic Parameters of Power Units
93-104
 
KRUKOVSKIY P.H., TADLIA O.Yu., DEINEKO A.I., SKLYARENKO D.I., OLIINYK V.S.
Kyiv Subway Tunnels Thermal and Humidity State Modeling
105-118

METHOD OF FORECASTING THE DYNAMICS OF MONTHLY ELECTRICITY PRODUCTION BY NPP UNITS OF UKRAINE

S.Ye. Saukh, A.V. Borisenko

Èlektron. model. 2019, 41(3):03-14

ABSTRACT

We present the results of the analysis of data on the installed capacities of the power units, theWe present the results of the analysis of data on the installed capacities of the power units, thevolumes of their electricity production, the frequency and duration of previously completed capitaland medium repairs of the main equipment of the NPP. A method of long-term forecasting ofschedules for the implementation of preventive maintenance of the main equipment of NPPs ofthe company SE “NNEGC Energoatom” and the volumes of electricity generation by NPP unitsis proposed. We present the results of using proposed method for forecasting the monthly dynamicsof the volumes of electricity generation by power units in modern conditions of nuclear powerdevelopment.

KEYWORDS

NPP power unit, repair, power generation, forecast.

REFERENCES

1. Available at: https://ua.boell.org/sites/default/files/transition_of_ukraine_to_the_renewable_
energy_by_2050_1.pdf
2. Available at: http://www.npp.zp.ua/Home/Production
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4. Available at: http://www.rnpp.rv.ua
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6. Available at: http://document.ua/pro-pidgotovku-obladnannja-elektrostancii-i-teplovih-merezh—
doc324051.html
7. Available at: http://consultant.parus.ua/?doc=0ANAN7F275
8. Available at: http://online.budstandart.com/ua/catalog/doc-page.html?id_doc=66114
9. Available at: http://consultant.parus.ua/?doc=09VFD68889
10. Available at: http://mpe.kmu.gov.ua/minugol/doccatalog/document?id=244950494
11. Available at: http://mpe.kmu.gov.ua/minugol/doccatalog/document?id=229324
12. Available at: http://zakon.rada.gov.ua/rada/show/v0578732-12/sp:max10
13. Available at: http://zakon.rada.gov.ua/rada/show/va006732-11
14. Skalozubov, V.I., Kovrizhkin, Yu.L. and Kolykhanov, V.N. (2008), Optimizatsiya planovykh
remontov energoblokov atomnykh elektrostantsiy s VVER [Optimization of scheduled
repairs of power units of nuclear power plants with VVER], Institute of NPP Safety Problems,
Kiev, Ukraine.
15. Yefimov, A.V., Potanina, T.V. and Kravets, V.L. (2012), “Application of interval statistics
methods for diagnosing the technical condition of equipment and planning the duration of repairs
of power units of thermal power plants and nuclear power plants”, Energosberezheniye.
Energetika. Energoaudit, no. 5, pp. 20-26.
16. Available at: https://zakon.rada.gov.ua/laws/show/z1224-16
17. Available at: http://www.rosenergoatom.ru/upload/iblock/376/37644e5aa93eec0702537
d60b9511e84.pdf
18. Available at: http://www.energoatom.kiev.ua/ua/press_centr-19/presentations-39/p/vistup_
prezidenta_energoatoma_uria_nedaskovs_kogo_na_komitets_kih_sluhannah_na_temu_
aderna_energetika_ukraini_vikliki_ta_perspektivi-3521
19. Available at: https://www.kmu.gov.ua/ua/npas/pro-shvalennya-tehniko-ekonomichnogoobgruntuvannya-
budivnictvo-energoblokiv-3-4-hmelnickoyi-aes-m-netishin-vul-energetikivkoriguvannya

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FAMILY OF SUBBOTIN DISTRIBUTIONS AND ITS CLASSIFICATION

A.I. Krasilnikov

Èlektron. model. 2019, 41(3):15-32

ABSTRACT

The properties of the probability density, its parameters, the central moments, and the cumulantThe properties of the probability density, its parameters, the central moments, and the cumulantcoefficients of the Subbotin distribution family were investigated. Based on the properties of thederivative of the probability density and cumulant coefficients, a classification of the Subbotinfamily of distributions was proposed, and criteria for choosing the probability density for approximatingthe distributions of non-Gaussian random variables were established.

KEYWORDS

family of Subbotin distributions, generalized Gaussian distribution, generalizedfamily of Subbotin distributions, generalized Gaussian distribution, generalizednormal distribution, error distribution.

REFERENCES

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3. Subbotin, M.T. (1923), “On the law of frequency of error”, Matematicheskii sbornik, Vol. 31, no. 2, pp. 296-301.
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https://doi.org/10.1080/02664760500079464
11. Pogany, T.K. and Nadarajah, S. (2010), “On the characteristic function of the generalized normal distribution”, Comptes Rendus Mathåmatique, 348(3), pp. 203-206.
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12. Crowder, G.E. and Moore, A.H. (1983), “Adaptive Robust Estimation Based on a Family of Generalized Exponential Power Distributions”, IEEE Transactions on Reliability, Vol. R-32, no. 5, pp. 488-495.
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14. Zabolotnii, S.V., Chepynoha, A.V., Bondarenko, Yu.Yu. and Rud, M.P. (2018), “Polynomial estimates of parameters for data with exponential power distribution”, Visnyk NTUU KPI. Seriia radiotekhnika, radioaparatobuduvannia, no. 75, pp. 40-47.
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ADAPTIVE METHOD OF IDENTIFICATION OF MODELS OF NONLINEAR DYNAMIC SYSTEMS WITH USING INTEGRAL VOLTERRA SERIES

V.A. Ivaniuk, V.A. Fedorchuk

Èlektron. model. 2019, 41(3):33-42

ABSTRACT

The article is devoted to the problem of identifying models of nonlinear dynamic systems, presentedThe article is devoted to the problem of identifying models of nonlinear dynamic systems, presentedin the form of Volterra integral series. When applying the traditional approach, the keyproblem is to conduct numerous necessary experiments, which grows in an exponential relationshipwith respect to the dimension of the nucleus. A significant number of experiments, in manycases, make it impossible to use the Volterra series method for studying nonlinear dynamical systems.An adaptive method for identifying models of nonlinear dynamic systems is proposed,which allows obtaining models in the form of Volterra series with preservation of adequacy withrespect to models obtained by the traditional method, but the number of necessary experiments tobuild nuclei is less by about a one order of magnitude. The proposed method is considered toidentify thetwo-dimensional kernel. The results can be extended to cases with higher-orderVolterra kernels.

KEYWORDS

dentification of nonlinear dynamic systems, Volterra series, adaptive method ofdentification of nonlinear dynamic systems, Volterra series, adaptive method ofidentification.

REFERENCES

1. Ivanyuk, V.A., Ponedilok, V.V. and Hryshchuk, V.A. (2014), “Computer realization deterministic method of identifying integrated models of nonlinear dynamic objects”, Zbirnyk naukovykh prats “Matematychne ta kompyuterne modelyuvannya”. Seriya: Tekhnichni nauky: Kamyanets-Podilskyy: Kamyanets-Podilskyy natsionalnyy universytet imeni Ivana Ohiyenka, no. 10, pp. 59-67.
2. Pupkov, K.A., Kapalin, V.I. and Yushchenko, A.S. (1976), Funktsionalnyye ryady v teorii nelineynykh sistem [Functional series in the theory of nonlinear systems], Nauka, Moscow, USSR.
3. Sidorov, D. (2013), Metody analiza integralnykh dinamicheskikh modeley: teoriya i prilozheniya [Methods of analysis of integral dynamic models: theory and applications], Izdatelstvo IGU, Irkutsk, Russia.

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Function Optimization Based on Higher-Order Quantum Genetic Algorithm

V.M. Tkachuk, Ph.D. (Phys.-Math.), M.I. Kozlenko, Ph.D (Eng.),
M.V. Kuz Dr.Sc. (Eng.), I.M. Lazarovych Ph.D. (Eng.), M.C. Dutchak
Vasyl Stefanyk Precarpathian National University
(57 Shevchenko str., Ivano-Frankivsk, 76018, Ukraine,
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.)

Èlektron. model. 2019, 41(3):43-58

ABSTRACT

Quantum genetic algorithms (QGA) are typically built using the traditional representation of theQuantum genetic algorithms (QGA) are typically built using the traditional representation of thequantum chromosome in the form of system of independent qubits. This makes it impossible touse a very powerful quantum calculations mechanism, namely quantum state entanglement. Inthis paper we implement a higher-order QGA and illustrate efficiency of the algorithm on the basisof example of optimization problem solved for a test functions set. An adaptive quantum gateoperator, which does not require a lookup table is also proposed. In comparison to traditionalQGA, the transition to higher (more than two) orders in the algorithm implementation showsmuch better results in terms of the running time, convergence speed and solution precision.

KEYWORDS

function optimization, quantum state entanglement, quantum genetic algorithm,function optimization, quantum state entanglement, quantum genetic algorithm,quantum computation, quantum register.

REFERENCES

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3. Zhang, G. (2011), “Quantum-inspired evolutionary algorithms: a survey and empirical study“, Journal of Heuristics, Vol. 2011, no. 17, pp. 303-351.
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