Practical Approach to Studying Evolutionary Methods for Setting Weight Coefficients of Artificial Neural Networks
https://doi.org/10.35596/1729-7648-2024-30-3-80-88
Abstract
The article describes the problems of developing neurocontrollers for controlling dynamic objects, including the complexity of forming training data sets. It is indicated that one of the known methods for training an artificial neural network controlling an object is the neuroevolutionary approach, which involves using a genetic algorithm to adjust the synaptic weighting coefficients of an artificial neural network. The idea of using a means of demonstrating the evolutionary approach to adjusting the weighting coefficients of an artificial neural network for practical training of students in the basics of the neuroevolutionary approach is proposed. Software has been developed to demonstrate the neuroevolutionary approach using the example of the evolution of an artificial neural network of a given structure intended to control a simplified computer model of an autonomous vehicle. A method for resolving the problem of stagnation when using the evolutionary approach to training an artificial neural network is described. Options for using the developed software in teaching students the basics of artificial intelligence technologies and evolutionary methods of multicriteria optimization are proposed.
About the Author
D. O. Petrov
Brest State Technical University
Belarus
Belarus
Dmitriy O. Petrov, Cand. of Sci., Associate Professor at the Department of Computer and Computer Sciences
Tel.: +375 29 523-87-23
224017, Brest, Moskovskaya St., 267
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Review
For citations:
Petrov D.O. Practical Approach to Studying Evolutionary Methods for Setting Weight Coefficients of Artificial Neural Networks. Digital Transformation. 2024;30(3):80-88. (In Russ.) https://doi.org/10.35596/1729-7648-2024-30-3-80-88
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