A Model-Based Approach to Multidimensional Interpolation Problems
DOI:
https://doi.org/10.22105/kmisj.v1i1.46Keywords:
Multidimensional technological object, interpolation problem, interpolation formula, empirical model, multidimensional interpolation algorithm, discrete informationAbstract
In this article, the problem of forming a sufficient source of information for the optimal management of a discretely defined multidimensional technological object was considered as a problem of multidimensional interpolation. An algorithm for developing a mathematical model of multidimensional interpolation using an empirical method is proposed. The idea of achieving interpolation accuracy based on the requirements for the adequacy of regression models was put forward. Based on the results obtained in the course of the research, it was possible to calculate the interpolation values of the received signal with very high accuracy according to several parameters of the multi-signal forecast models belonging to the same system. Methodologically, the main focus of the research is on building an interpolation function for an object with many parameters and a sufficiently large amount of information and its success.
References
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M.B. Lee et al., Some issues on interpolation matrices of locally scaled radial basis functions, Applied Mathematics and Computation, 217:10, 5011–5014 (2011).
V. Dharmadasa et al., A new interpolation method to resolve under-sampling of UAV-lidar snow depth observations in coniferous forests, Cold Regions Science and Technology, 220, 104134 (2024).
X. Chen et al., Improving the accuracy of wind speed spatial interpolation: A pre-processing algorithm for wind speed dynamic time warping interpolation, Energy, 295, 130876 (2024).
H. Hachiya et al., Position-dependent partial convolutions for supervised spatial interpolation, Machine Learning with Applications, 14, 100514 (2023).
R. Cavoretto et al., Numerical cubature on scattered data by adaptive interpolation, Journal of Computational and Applied Mathematics, 444, 115793 (2024).
D. Li et al., Direct cubic B-spline interpolation: A fuzzy interpolating method for weightless, robust, and accurate DVC computation, Optics and Lasers in Engineering, 172, 107886 (2024).
M. Sveda et al., When spatial interpolation matters: Seeking an appropriate data transformation from the mobile network for population estimates, Computers, Environment and Urban Systems, 110, 102106 (2024).
M. Lindemulder et al., A discrete framework for the interpolation of Banach spaces, Advances in Mathematics, 440, 109506 (2024).
Y. Zhang et al., A comparative study of different radial basis function interpolation algorithms in the reconstruction and path planning of γ-radiation fields, Nuclear Engineering and Technology, 56, (2024).
S. Zafeiris et al., An overset interpolation algorithm for multi-phase flows using 3D multiblock polyhedral meshes, Computers & Mathematics with Applications, 161, 155–173 (2024).
A. Liu et al., Screening and optimization of interpolation methods for mapping soil-borne polychlorinated biphenyls, Science of The Total Environment, 913, 169498 (2024).
P.D. Kaklis et al., Shape-preserving interpolation on surfaces via variable-degree splines, Computer Aided Geometric Design, 109, 102276 (2024).
T. Lamby, S. Nicolay, Interpolation with a function parameter from the category point of view, Journal of Functional Analysis, 286:3, 110249 (2024).
R. Pasupathi et al., A very general framework for fractal interpolation functions, Journal of Mathematical Analysis and Applications, 534:2, 128093 (2024).
F.D. Jörayev, M.A. Ochilov, Algorithms for multi-factory polynomial modeling of technological processes, Chemical Technology, Control and Management, 2023:1, 2181–1105 (2023).
A.N. Rakhimov, F.D. Jörayev, A systematic approach to the methodology of agricultural development and the strategy of econometric modeling, Res Militaris, 12:4, 2164–2174 (2022).
A.K. Juraev et al., Nonlinear control object identification problems: Methods and approaches, E3S Web of Conferences, 392, 02043 (2023).
F.D. Juraev et al., Algorithms for improving the process of modeling complex systems based on big data: On the example of regional agricultural production, E3S Web of Conferences, 392, 01050 (2023).
F.D. Juraev et al., Analysis of functions of belonging and assessment of the state of the control object, World Economics and Finance Bulletin, 23, 85–90 (2023).
F. Juraev, Perspective problems of development of agricultural production and their econometric modeling, Economics and Education, 4, 377–385 (2021).
I. Khalismatov et al., Correlation analysis of geological factors with the coefficient of gas transfer of organizations, E3S Web of Conferences, 497, 01018 (2024).
F.D. Juraev, A model approach to long-term forecasting of electricity supply, Journal of Integrated Education and Research, 2:10, 41–45 (2023).
F.D. Juraev et al., Problems of Management of Technological Systems Under Uncertainty: Models and Algorithms, Global Scientific Review, 19, 39–48 (2023).
F. Jörayev, Agrocluster system optimization methods: Uncertainty minimization using algorithm and model, Economics and Education, 24:6, 306–314 (2023).
F.D. Jörayev et al., Algorithms for improving models of optimal control for multi-parametric technological processes based on artificial intelligence, E3S Web of Conferences, 460, 04013 (2023).
A.R. Mallaev et al., Improvement of control models of closed systems using neural networks, Innovative Technologies, 51:3, 12–26 (2023).
M.A. Ochilov et al., Analysis of important factors in checking the optimality of an indeterminate adjuster in a closed system, Journal of Critical Review, 7:15, 1679–1684 (2020).