Preview

Proceedings in Cybernetics

Advanced search

Сalculation of parameters estimates in homogeneous nested piecewise linear regression with alternating min and max functions

https://doi.org/10.35266/1999-7604-2025-1-10

Abstract

The article provides a brief review of publications on the implementation of nonlinear model forms in mathematical modeling of complex technical and socio-economic objects. Specifically, the following are considered: the dynamics of a nonlinear system with two degrees of freedom, consisting of a grounded
linear oscillator connected to a light mass by a substantially nonlinear and nonlinearizable stiffness; the description of a new cybernetic structure that can help in understanding the specifics of timely deployment of
recurring social phenomena; a new mathematical model for managing cyclical unemployment; configuration
of several renewable energy systems in an eco-industrial park, an economic and mathematical model of production planning that takes into account its scale and the release of defective products. Homogeneous nested piecewise linear regressions with alternating min and max functions are studied. The tasks of calculating parameter estimates by minimizing the sums of absolute values of approximation errors are reduced to linear Boolean programming problems. The obtained optimal values of the Boolean variables of the problem reveal the order of external minimum and internal maximum in the considered nested models. A numerical illustrative case is solved.

About the Authors

S. I. Noskov
Irkutsk State Transport University, Irkutsk
Russian Federation

Professor, Doctor of Sciences (Engineering)

 



S. V. Belyaev
Irkutsk State Transport University, Irkutsk
Russian Federation

Master’s Degree Student



References

1. Neydorf R. Bivariate “Cut-Glue” approximation of strongly nonlinear mathematical models based on experimental data // SAE: International Journal of Aerospace. 2015. Vol. 8, no. 1. P. 47–54. https://doi.org/10.4271/2015-01-2394.

2. Lee Y. S., Kerschen G., Vakakis A. F. et al. Complicated dynamics of a linear oscillator with a light, essentially nonlinear attachment // Physica D: Nonlinear Phenomena. 2005. Vol. 204, no. 1–2. P. 41–69. https://doi.org/10.1016/j.physd.2005.03.014.

3. Porubov A. V., Aero E. L., Maugin G. A. Two approaches to study essentially nonlinear and dispersive properties of the internal structure of materials // Physical Review E. 2009. Vol. 79, no. 4. https://doi.org/10.1103/PhysRevE.79.046608.

4. Devezas T. C., Corredine J. T. The nonlinear dynamics of technoeconomic systems: An informational interpretation // Technological Forecasting and Social Change. 2002. Vol. 69, no. 4. P. 317–357. https://doi.org/10.1016/S0040-1625(01)00155-X.

5. Zhu H., Xiao X., Huang X. et al. Time-lead nonlinear grey multivariable prediction model with applications // Applied Mathematical Modelling. 2023. Vol. 123. P. 464–483. https://doi.org/10.1016/j.apm.2023.07.003.

6. Yahyaoui M. E., Amine S. Mathematical modeling of unemployment dynamics with skills development and cyclical effects // Partial Differential Equations in Applied Mathematics. 2024. Vol. 11. https://doi.org/10.1016/j.padiff.2024.100800.

7. Misrol M. A., Alwi S. R. W., Lim J. S. et al. Optimising renewable energy at the eco-industrial park: A mathematical modelling approach // Energy. 2022. Vol. 261.https://doi.org/10.1016/j.energy.2022.125345.

8. Khamiduulin M. R., Isavnin A. G. Economy of Scale and Production of Rejects in the Production Planning Model // Mediterranean Journal of Social Sciences. 2015. Vol. 6, no. 2. P. 267–276. https://doi.org/10.5901/mjss.2015.v6n2p267.

9. Teru A. H., Koya P. R. Mathematical modelling of deforestation of forested area due to lack of awareness of human population and its conservation // Mathematical Modelling and Applications. 2020. Vol. 5, no. 2. P. 94–104. https://doi.org/10.11648/j.mma.20200502.15.

10. Aliyev A. N-component piecewise-linear models: Enhancing economic event prediction through software // Advanced Journal of Applied Mathematics and Statistics. 2023. Vol. 11, no. 1. P. 8–32.

11. Носков С. И. Подход к формализации вложенной кусочно-линейной регрессии // Международный журнал гуманитарных и естественных наук. 2023. Т. 1–2, № 76. С. 218–220. https://doi.org/10.24412/2500-1000-2023-1-2-218-220.

12. Носков С. И. Некоторые формы вложенной кусочно-линейной регрессии // Известия ТулГУ. Технические науки. 2023. № 3. С. 467–469.

13. Носков С. И., Белинская С. И. Вычисление оценок параметров однородной вложенной кусочно-линейной регрессии // Вестник Дагестанского государственного технического университета. Технические науки. 2023. Т. 50, № 4. С. 115–120. https://doi.org/10.21822/2073-6185-2023-50-4-115-120.

14. Bentobache M., Bibi M. O. A two-phase support method for solving linear programs: numerical experiments // Mathematical Problems in Engineering. 2012. Vol. 2012, no. 1. https://doi.org/10.1155/2012/482193.


Review

For citations:


Noskov S.I., Belyaev S.V. Сalculation of parameters estimates in homogeneous nested piecewise linear regression with alternating min and max functions. Proceedings in Cybernetics. 2025;24(1):68-73. (In Russ.) https://doi.org/10.35266/1999-7604-2025-1-10

Views: 117


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1999-7604 (Online)