Transformation of Mathematical Model for Complex Object in Form of Interval Difference Equations to a Differential Equation

Authors

  • Mykola Dyvak
  • Roman Pasichnyk
  • Andriy Melnyk
  • Andriy Dyvak
  • Frank Otoo

DOI:

https://doi.org/10.47839/ijc.22.2.3091

Keywords:

interval differential equations, structural identification, interval model, Taylor series

Abstract

Mathematical models of complex objects in the form of interval difference equations are built on the basis of the obtained experimental interval data within the limits of the inductive approach. At the same time, interpretation of physical properties of the object on the base of such model is complex enough. A method of transformation of a mathematical model in the form of interval differential equations was proposed in the article. The proposed method is based on the formulas for representing the values of the function at the node of the difference grid in the Taylor series in the neighborhood of the base node, as well as the differential representation of the derivatives in the same neighborhood. The developed approach creates opportunities for the identification of interval models of complex objects based on the analysis of interval data with further interpretation of the physical properties of the modeled object according to the classical scheme.

References

W. Goddard, & S. Melville, Research Methodology: An Introduction, 2nd edition, Blackwell Publishing, 2004.

H. R. Bernard, Research Methods in Anthropology, 5th edition, AltaMira Press, 2011.

A. G. Ivakhnenko, “Group method of data handling - A rival of the method of stochastic approximation,” Soviet Automatic Control, no. 13, pp. 43-71, 1966.

M. Saunders, P. Lewis, & A. Thornhill, Research Methods for Business Students, 6th edition, Pearson Education Limited, 2012.

W. L. Neuman, Social Research Methods: Qualitative and Quantitative Approaches, Allyn and Bacon, 2003.

M. G. Lodico, D. T. Spaulding, & K. H. Voegtle, Methods in Educational Research: From Theory to Practice, John Wiley & Sons, 2010.

O. Dag, C. Yozgatligil, “GMDH: An R package for short term forecasting via GMDH type neural network algorithms,” The R Journal, vol. 8, issue 1, pp. 379-386, 2016. https://doi.org/10.32614/RJ-2016-028.

T. Kondo, J. Ueno, “Revised GMDH-type neural network algorithm with a feedback loop identifying sigmoid function neural network,” International Journal of Innovative Computing, Information and Control, vol. 2, issue 5, pp. 985-996, 2006. https://doi.org/10.5687/sss.2006.137.

W.-Y. Sun and C.-Z. Chang, “Diffusion model for a convective layer. Part 2: Plume released from a continuous point source,” J. Climate Appl. Meteorol., vol. 25, no. 10, pp. 1454-1463, 1986. https://doi.org/10.1175/1520-0450(1986)025<1454:DMFACL>2.0.CO;2.

F. Pasquill, “Atmospheric dispersion parameters in Gaussian plume modeling: [part II. Possible Requirements for Change in the Turner Workbook Values],” EPA-600/4-76-030b, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, 1976.

P. S. Venhersʹkyy, O. R. Demkovych, “Construction of a mathematical model of the process of liquid filtration in the soil,” Bulletin of Lviv university, Series Applied Mathematics Informatics, issue 15, pp. 170-177, 2009. (in Ukrainian)

I. Kopas, Differential equations. Kyiv: Igor Sikorsky Kyiv Polytechnic Institute, 2018, 126 p.

O. Zyubanov, Differential Equations, Vasyl’ Stus Donetsk National University, 2018, 72 p.

W.-S. Wang “Discrete transform methods of solutions of the fractional difference equations,” Proceedings of the 2018 5th International Conference on Information Science and Control Engineering, ICISCE, 2019, pp. 714-717. https://doi.org/10.1109/ICISCE.2018.00153.

M. Dyvak, I. Voytyuk, N. Porplytsya and A. Pukas, “Modeling the process of air pollution by harmful emissions from vehicles,” Proceedings of the 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), Slavske, 2018, pp. 1272-1276, https://doi.org/10.1109/TCSET.2018.8336426.

M. Dyvak, N. Porplytsya, “Formation and identification of a model for recurrent laryngeal nerve localization during the surgery on neck organs,” Advances in Intelligent Systems and Computing III. CSIT 2018, Cham: Springer, vol. 871, pp. 391-404, 2019. https://doi.org/10.1007/978-3-030-01069-0_28.

Y.-H. Lee, & S. J. Haberman, “Studying score stability with a harmonic regression family: A comparison of three approaches to adjustment of examinee-specific demographic data,” Journal of Educational Measurement, vol. 58, issue 1, pp. 54–82, 2021. https://doi.org/10.1111/jedm.12266.

A. Agresti, Categorical Data Analysis, (second ed.), Wiley, New York, 2002. https://doi.org/10.1002/0471249688.

M. Analla “Model validation through the linear regression fit to actual versus predicted values,” Agricultural Systems, vol. 57, pp. 115-119, 1998. https://doi.org/10.1016/S0308-521X(97)00073-5.

J. Bibby, H. Toutenburg, Prediction and Improved Estimation in Linear Models, Wiley, Berlin, 1977.

M. Dyvak, “Parameters identification method of interval discrete dynamic models of air pollution based on artificial bee colony algorithm,” Proceedings of the 2020 10th IEEE International Conference on Advanced Computer Information Technologies (ACIT), Deggendorf, Germany, 2020, pp. 130-135. https://doi.org/10.1109/ACIT49673.2020.9208972.

C. Sun, et al., Stochastic prediction of multi-agent interactions from partial observations, arXiv preprint arXiv:1902.09641, 2019.

J. C. Bansal, P. K. Singh, N. R. Pal, “Evolutionary and swarm intelligence algorithms,” Springer Cham, 2019, 190 p. https://doi.org/10.1007/978-3-319-91341-4.

J. Tang, G. Liu and Q. Pan, “A review on representative swarm intelligence algorithms for solving optimization problems: Applications and trends,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 10, pp. 1627-1643, 2021. https://doi.org/10.1109/JAS.2021.1004129.

A. Mortazavi, V. Toğan, M. Moloodpoor, “Solution of structural and mathematical optimization problems using a new hybrid swarm intelligence optimization algorithm,” Advances in Engineering Software, vol. 127, 2019, pp. 106-123. https://doi.org/10.1016/j.advengsoft.2018.11.004.

H. Wang, W. Wang, X. Zhou, “Artificial bee colony algorithm based on knowledge fusion,” Complex Intell. Syst., vol. 7, pp. 1139–1152, 2021. https://doi.org/10.1007/s40747-020-00171-2.

D. Bajer, B. Zorić, “An effective refined artificial bee colony algorithm for numerical optimisation,” Information Sciences, vol. 504, pp. 221-275, 2019. https://doi.org/10.1016/j.ins.2019.07.022.

K. Hussain, M. N. M. Salleh, Shi Cheng, Yu. Shi, R. Naseem, “Artificial bee colony algorithm: A component-wise analysis using diversity measurement,” Computer and Information Sciences, vol. 32, issue 7, pp. 794-808, 2020. https://doi.org/10.1016/j.jksuci.2018.09.017.

R. Aguejdad, “The influence of the calibration interval on simulating non-stationary urban growth dynamic using CA-Markov model,” Remote Sensing, vol. 13, issue 3, pp. 468, 2021. ttps://doi.org/10.3390/rs13030468.

N. T. Thong, L. Q. Dat, L. H. Son, N. D. Hoa, M. Ali, and F. Smarandache, “Dynamic interval valued neutrosophic set: modeling decision making in dynamic environments,” Computers in Industry, vol. 108, pp. 45–52, 2019. https://doi.org/10.1016/j.compind.2019.02.009.

L. Zhu et al., “Analytic interval prediction of power system dynamic under interval uncertainty,” Journal of Physics: Conference Series, vol. 2427, 2022, 2nd International Conference on Smart Grid and Energy Internet (SGEI 2022) 21/10/2022 - 23/10/2022 Sanya, China, pp.1-15.

M. J. Simpson, S. A. Walker, E. N. Studerus, S. W. McCue, R. J. Murphy, O. J. Maclaren, “Profile likelihood-based parameter and predictive interval analysis guides model choice for ecological population dynamics,” Mathematical Biosciences, vol. 355, 108950, 2023. https://doi.org/10.1016/j.mbs.2022.108950.

M. Dyvak, A. Pukas, A., I. Oliynyk, A. Melnyk, “Selection the 'Saturated' block from interval system of linear algebraic equations for recurrent laryngeal nerve identification,” Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP’2018, 2018, pp. 444–448. https://doi.org/10.1109/DSMP.2018.8478528.

X. Jiang, and Z. Bai, “Interval uncertainty quantification for the dynamics of multibody systems combing bivariate Chebyshev polynomials with local mean decomposition,” Mathematics, vol. 10, no. 12, 1999. https://doi.org/10.3390/math10121999.

N. R. Kondratenko, O. O. Snihur, “Investigating adequacy of interval type-2 fuzzy models in complex objects identification problems,” System Research and Information Technologies, no. 4, pp. 94–104, 2019. https://doi.org/10.20535/SRIT.2308-8893.2019.4.10.

Downloads

Published

2023-07-02

How to Cite

Dyvak, M., Pasichnyk, R., Melnyk, A., Dyvak, A., & Otoo, F. (2023). Transformation of Mathematical Model for Complex Object in Form of Interval Difference Equations to a Differential Equation. International Journal of Computing, 22(2), 219-224. https://doi.org/10.47839/ijc.22.2.3091

Issue

Section

Articles