INVESTIGATION OF PARAMETERS OF THE PROBLEM OF SPLINE APPROXIMATION OF NOISY DATA BY NUMERICAL METHODS OF OPTIMAL CONTROL
DOI:
https://doi.org/10.14529/ctcr210314Keywords:
citation system, scientometric methods, aggregation of bibliographic information, modification of the Winnowing method, Levenshtein method, shingle methodAbstract
Currently, the problems of distortion of measurement data by noise and the appearance of uncertainties in quality criteria have caused increased interest in research in the field of spline approximation. At the same time, existing methods of minimizing empirical risk, assuming that
the noise is a uniform distribution with heavier tails than Gaussian, limit the scope of application of these studies. The problem of estimating noise-distorted data is usually based on solving an optimization problem with a function containing uncertainty arising from the problem of finding
optimal parameters. In this regard, the estimation of distorted noise cannot be solved by classical methods. Aim. This study is aimed at solving and analyzing the problem of spline approximation of data under uncertainty conditions based on the parametrization of control and the gradient projection algorithm. Methods. The study of the problem of spline approximation of noisy data is carried out by the method of approximation of the piecewise constant control function. In this case, parametrization of the control is possible only for a finite number of break points of the first kind. In the framework of the experimental study, the gradient projection algorithm is used for
the numerical solution of the spline approximation problem. The proposed methods are used to study the parameters of the problem of spline approximation of data under conditions of uncertainty. Results. The numerical study of the control parametrization approach and the gradient projection
algorithm is based on the developed software and algorithmic tool for solving the problem of
the spline approximation model under uncertainty. To evaluate the noise-distorted data, numerical experiments were conducted to study the model parameters and it was found that increasing the value of the parameter α leads to an increase in accuracy, but a loss of smoothness. In addition, the analysis showed that the considered distribution laws did not change the accuracy and convergence rate of the algorithm. Conclusion. The proposed approach for solving the problem of spline approximation under uncertainty conditions allows us to determine the problems of distortion of measurement data by noise and the appearance of uncertainties in the quality criteria. The study of the model parameters showed that the constructed system is stable to the error of the initial approximation, and the distribution laws do not significantly affect the accuracy and convergence of the gradient projection method.
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