1. Главная
  2. Публикации
  3. Recurrent Neural Network Models Based on Optimization Methods

Recurrent Neural Network Models Based on Optimization Methods

Описание

Тип публикации: статья из журнала

Год издания: 2022

Идентификатор DOI: 10.3390/math10224292

Ключевые слова: dynamic system, linear matrix equations, optimization methods, zeroing neural network

Аннотация: Many researchers have addressed problems involving time-varying (TV) general linear matrix equations (GLMEs) because of their importance in science and engineering. This research discusses and solves the topic of solving TV GLME using the zeroing neural network (ZNN) design. Five new ZNN models based on novel error functions arisinПоказать полностьюg from gradient-descent and Newton optimization methods are presented and compared to each other and to the standard ZNN design. Pseudoinversion is involved in four proposed ZNN models, while three of them are related to Newton’s optimization method. Heterogeneous numerical examples show that all models successfully solve TV GLMEs, although their effectiveness varies and depends on the input matrix. © 2022 by the authors.

Ссылки на полный текст

Издание

Журнал: Mathematics

Выпуск журнала: Vol. 10, Is. 22

Номера страниц: 4292

ISSN журнала: 22277390

Издатель: MDPI

Персоны

  • Stanimirović P.S. (Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, Niš, 18000, Serbia, Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Krasnoyarsk, 660041, Russian Federation)
  • Mourtas S.D. (Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Krasnoyarsk, 660041, Russian Federation, Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Sofokleous 1 Street, Athens, 10559, Greece)
  • Katsikis V.N. (Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Sofokleous 1 Street, Athens, 10559, Greece)
  • Kazakovtsev L.A. (Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Krasnoyarsk, 660041, Russian Federation)
  • Krutikov V.N. (Department of Applied Mathematics, Kemerovo State University, Krasnaya Street 6, Kemerovo, 650043, Russian Federation)

Вхождение в базы данных