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- W4311487222 abstract "Most methods of analysis and synthesis of control assume the known mathematical model of the control object. To obtain the desired control result, the model must accurately describe the real processes [1]. It is often not possible to obtain quantitative relations between the parameters and variables of the system under consideration on the basis of theoretical laws. For many causal systems, these relations can only be assessed experimentally, during identification. The most developed approach is the so-called stochastic identification, which consists in obtaining unbiased estimates of the parameters for certain model. However, the possibilities of such identification are significantly limited in the case of complex systems. This chapter discusses a more general approach to identification within the regression model class, which searches for approximate solutions that are consistent in accuracy with errors in the data. Since identification problems in complex systems are often incorrectly posed, the proposed method includes regularization that provides practically suitable solutions." @default.
- W4311487222 created "2022-12-26" @default.
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- W4311487222 date "2022-12-14" @default.
- W4311487222 modified "2023-10-17" @default.
- W4311487222 title "Identification of Complex Systems in the Class of Linear Regression Models" @default.
- W4311487222 doi "https://doi.org/10.1201/9781003339229-10" @default.
- W4311487222 hasPublicationYear "2022" @default.
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