UDC 519.2, 519.61, 519.71
1 Space Research Institute of the National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kyiv, Ukraine
 v.f.gubarev@gmail.com
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2 Space Research Institute of the National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kyiv, Ukraine
salnikov.nikolai@gmail.com
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3 Space Research Institute of the National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kyiv, Ukraine
sergvik@ukr.net
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IDENTIFICATION OF REGULARIZED MODELS IN THE LINEAR REGRESSION CLASS
Abstract. Identification of complex discrete systems in the class of linear regression models is considered.
The problem of identifying an exact model on noisy initial data is known to be ill-posed. This is especially significant when using high-dimensional models.
Within the guaranteed approach to uncertainty used in the article, it is proposed to use the dimension of the model as a regularization parameter.
Here we suggested and tested two techniques for estimating optimal dimension and parameters of linear regression model
to ensure its consistency in accuracy with the data error. Numerical simulations were carried out and their efficiency was evaluated.
Keywords: identification, linear regression, complex system, regularization, model dimension estimation, SVD, simulation.
FULL TEXT
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