Improving the Accuracy of Neural Network Pattern Recognition by Fractional Gradient Descent

Improving the Accuracy of Neural Network Pattern Recognition by Fractional Gradient Descent

In this paper we propose the fractional gradient descent for increasing the training and work of modern neural networks. This optimizer searches the global minimum of the loss function considering the fractional gradient directions achieved by Riemann-Liouville, Caputo, and Grunwald-Letnikov derivat...

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Dettagli Bibliografici
Autori principali:	Abdulkadirov, R. I., Абдулкадиров, Р. И., Lyakhov, P. A., Ляхов, П. А., Baboshina, V. A., Бабошина, В. А., Nagornov, N. N., Нагорнов, Н. Н.
Natura:	Статья
Lingua:	English
Pubblicazione:	Institute of Electrical and Electronics Engineers Inc. 2024
Soggetti:	Caputo Stochastic gradient descent Convolutional neural networks Fractional derivatives of Riemann-Liouville Grunwald-Letnikov Multilayer perceptron Optimization algorithms
Accesso online:	https://dspace.ncfu.ru/handle/123456789/29336
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