Various length step calculation method for piecewise linear approximation problem of empirical nonlinear function with a specified accuracy
Vestnik Tverskogo gosudarstvennogo universiteta. Seriâ Prikladnaâ matematika, no. 3 (2022), pp. 35-48 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

The paper proposes a numerical method for adaptive selection of a variable step for approximating a nonlinear one-dimensional function, the analytical expression of which is not given, by a piecewise linear function. It is shown that under the conditions of miniaturization of computing devices, the selection of the approximation step (grid) is an important task in terms of minimizing the required number of calculations. The developed algorithm includes the calculation of the lengths of successive intervals, which eventually cover the entire domain of the function, with a predetermined approximation accuracy. The coefficient of determination is used as a measure of accuracy. Numerical experiments are presented, the proposed method is compared with the method with a constant step, providing the same accuracy, also expressed in the value of the coefficient of determination. The conducted computational experiment proved the advantage of the developed method in terms of computational costs with the same accuracy.
Keywords: piecewise linear approximation, variable step, approximation grid step, numerical methods.
@article{VTPMK_2022_3_a2,
     author = {Z. Z. Mingaliyev and S. V. Novikova and G. V. Moiseyev},
     title = {Various length step calculation method for piecewise linear approximation problem of empirical nonlinear function with a specified accuracy},
     journal = {Vestnik Tverskogo gosudarstvennogo universiteta. Seri\^a Prikladna\^a matematika},
     pages = {35--48},
     year = {2022},
     number = {3},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VTPMK_2022_3_a2/}
}
TY  - JOUR
AU  - Z. Z. Mingaliyev
AU  - S. V. Novikova
AU  - G. V. Moiseyev
TI  - Various length step calculation method for piecewise linear approximation problem of empirical nonlinear function with a specified accuracy
JO  - Vestnik Tverskogo gosudarstvennogo universiteta. Seriâ Prikladnaâ matematika
PY  - 2022
SP  - 35
EP  - 48
IS  - 3
UR  - http://geodesic.mathdoc.fr/item/VTPMK_2022_3_a2/
LA  - ru
ID  - VTPMK_2022_3_a2
ER  - 
%0 Journal Article
%A Z. Z. Mingaliyev
%A S. V. Novikova
%A G. V. Moiseyev
%T Various length step calculation method for piecewise linear approximation problem of empirical nonlinear function with a specified accuracy
%J Vestnik Tverskogo gosudarstvennogo universiteta. Seriâ Prikladnaâ matematika
%D 2022
%P 35-48
%N 3
%U http://geodesic.mathdoc.fr/item/VTPMK_2022_3_a2/
%G ru
%F VTPMK_2022_3_a2
Z. Z. Mingaliyev; S. V. Novikova; G. V. Moiseyev. Various length step calculation method for piecewise linear approximation problem of empirical nonlinear function with a specified accuracy. Vestnik Tverskogo gosudarstvennogo universiteta. Seriâ Prikladnaâ matematika, no. 3 (2022), pp. 35-48. http://geodesic.mathdoc.fr/item/VTPMK_2022_3_a2/

[1] Loran P. Zh., Approximation and optimization, Mir Publ., Moscow, 1975, 496 pp. (in Russian)

[2] Sohn J., Robertazzi T. G., Luryi S., “Optimizing computing costs using divisible load analysis”, IEEE Transactions on Parallel and Distributed Systems, 9:3 (1998), 225–234 | DOI

[3] Bauman E. V., Goldovskaya M. D., Dorofeyuk Yu. A., “Piecewise linear approximation methods and their use in control problems”, Tauride Bulletin of Computer Science and Mathematics, 2008, no. 1 (12), 73–79 (in Russian)

[4] Tomek I., “Two Algorithms for Piecewise-Linear Continuous Approximation of Functions of One Variable”, IEEE Transactions on Computers, C-23:4 (1974), 445–448 | DOI

[5] Budylina E. A., Garkina I. A., Sukhov Ya. I., “Piecewise linear approximation algorithm with maximum interval”, Young scientist, 2014, no. 3 (62), 269–271 (in Russian)

[6] Paasonen V. I., “A third-order approximation scheme on an uneven grid for the Navier-Stokes equations”, Computing technologies, 5:5 (2000), 78–85 (in Russian)

[7] Novikova S. V., Mingaliev Z. Z., “Neural network prediction of glycemia in patients with diabetes mellitus based on mixed time series with the prospect of use as part of an intelligent insulin pump”, Modern information technologies and IT education, 17:1 (2021), 90–98 (in Russian) | DOI

[8] Types of Insulin https://www.cdc.gov/diabetes/ basics/type-1-types-of-insulin.html

[9] Toffanin Ch., Zisser H., Doyle F. J., Dassau E., “Dynamic Insulin on Board: Incorporation of Circadian Insulin Sensitivity Variation”, Journal of Diabetes Science and Technology, 7:4 (2013), 928–940 | DOI

[10] Vettoretti M., Cappon G., Facchinetti A., Sparacino G., “Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors”, Sensors, 20:14 (2020), 3870 | DOI

[11] Cormen T. H., Leiserson C. E., Rivest R. L., Stein C., Introduction To Algorithms, The MIT Press, Cambridge, Massachusetts, 2009, 1313 pp.