Methods for Constructing Artificial Neural Networks for Data Classification

L. V. Serebryanaya ()

Digital Transformation, 2022, vol. 28, issue 1

Abstract: The features of the organization of distance learning of students in a higher educational institution, as well as the information and educational technologies necessary for this, are considered. A system of automatic assessment of students’ knowledge is proposed. It is based on a model in the form of an artificial neural network. The features of such a model are given. The two implemented methods for constructing artificial neural networks have been used in the software module for testing students’ knowledge. The choice of the type of network, its structure, and parameters has been substantiated. The first method is related to the construction of an artificial neural network in the manual mode. An algorithm is presented that reflects the iterative process of its training. In the second case, the network is built automatically by applying a genetic algorithm. At the beginning of the work, a set of randomly generated initial data arrives at the input of the algorithm. In the course of its work, the genetic algorithm determines the architecture and parameters of the neural network, which ensure the successful solution of the assigned applied problem. Trained networks are used to classify data. Both networks showed acceptable classification accuracy of the results obtained in the course of the students’ knowledge testing.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:abx:journl:y:2022:id:657

DOI: 10.35596/2522-9613-2022-28-1-20-26

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