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Models of Recognition Algorithms Based on Construction of Two-Dimensional Logical Classifiers

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Networked Control Systems for Connected and Automated Vehicles (NN 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 510))

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Abstract

The paper considers issues related to the construction of a model of recognition algorithms (MRA) designed to solve the problem of object classification in conditions of interconnectedness of features. A new approach to the construction of the MRA is proposed on the basis of the construction of two-dimensional threshold classifiers (TDTC). The main idea of the proposed MRAs is to form a set of preferred two-dimensional classifiers. A distinctive feature of the proposed model is to determine a suitable set of TDTCs when constructing an extreme recognition algorithm (RA). The purpose of this paper is to develop MRAs based on the construction of a TDTC in the subspace of representative features. In scientific terms, the results of this work together represent a new solution to a scientific problem related to the issues of increasing the reliability of RA based on the construction of two-dimensional threshold classifiers. The practical significance of the results lies in the fact that the developed MRAs can expand the area of their application in the context of the interconnectedness of characteristics.

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Authors and Affiliations

  1. Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Yunusabadsky District, Amir Temur Avenue, 108, Tashkent, 100084, Uzbekistan

    Gulmira Mirzaeva

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  1. Gulmira Mirzaeva
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Correspondence to Gulmira Mirzaeva .

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Editors and Affiliations

  1. Rostovskogo Strelkovogo Polka Narodnogo, Rostov State Transport University, Rostov-on-Don, Russia

    Alexander Guda

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Mirzaeva, G. (2023). Models of Recognition Algorithms Based on Construction of Two-Dimensional Logical Classifiers. In: Guda, A. (eds) Networked Control Systems for Connected and Automated Vehicles. NN 2022. Lecture Notes in Networks and Systems, vol 510. Springer, Cham. https://doi.org/10.1007/978-3-031-11051-1_122

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