The Multi-Objective Optimization of Complex Objects Neural Network Models
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URI (для ссылок/цитирований):
https://elib.sfu-kras.ru/handle/2311/33018Автор:
Тынченко, В. С.
Тынченко, В. В.
Бухтояров, В. В.
Тынченко, С. В.
Петровский, Э. А.
Коллективный автор:
Институт управления бизнес-процессами и экономики
Институт нефти и газа
Институт космических и информационных технологий
Кафедра экономики и информационных технологий менеджмента
Кафедра технологических машин и оборудования нефтегазового комплекса
Кафедра информатики
Дата:
2016-08Журнал:
Indian Journal of Science and TechnologyКвартиль журнала в Scopus:
Q2Библиографическое описание:
Тынченко, В. С. The Multi-Objective Optimization of Complex Objects Neural Network Models [Текст] / В. С. Тынченко, В. В. Тынченко, В. В. Бухтояров, С. В. Тынченко, Э. А. Петровский // Indian Journal of Science and Technology. — 2016. — Т. 9 (№ 29).Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.
Аннотация:
Background/Objectives: The study considers the modeling technique that applies artificial neural networks
analyzing their types and functional principles. Methods: A comparative analysis of the existing methods of structural and
parametric synthesis of artificial neural networks has been carried out; the practicability of applying evolutionary approach
to solve this problem has been justified. Findings: The multi-objective optimization of the structure of a neural network
model has been formalized, given its computational complexity.
The genetic algorithm has been adjusted to solve the problems of unconditional optimization of the parameters of the neural network and of selecting its effective structure in multi-objective setting. The results of solving the practical
problem prove that the application of the suggested approach can help alleviate the computational complexity of the obtained structures of artificial neural networks.
Applications/Improvements: The results of the study make it possible for a decision maker to select neural network model among multiple options, given the required precision and the available computational resources.
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