Abstract
The article presents the regularized algorithms for identification of uncertain dynamic management objects. Various algorithms for identifying dynamic control objects are analyzed in the conditions of approximate assignment of source data. Regularization, moderate spoilage, and imaginary shift methods for extended systems are presented. The results of solving various model problems for different error levels of the initial data are shown. It is shown that the best indicators are provided by the simplified regularization method for an extended system of equations, since this extended system is by definition symmetric and positive definite.
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Igamberdiev, H.Z., Mamirov, U.F. (2021). Regular Algorithms for the Parametric Estimation of the Uncertain Object Control. In: Aliev, R.A., Yusupbekov, N.R., Kacprzyk, J., Pedrycz, W., Sadikoglu, F.M. (eds) 11th World Conference “Intelligent System for Industrial Automation” (WCIS-2020). WCIS 2020. Advances in Intelligent Systems and Computing, vol 1323. Springer, Cham. https://doi.org/10.1007/978-3-030-68004-6_42
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