Fractional Derivative in LSTM Networks: Adaptive Neuron Shape Modeling with the Grünwald–Letnikov Method

The incorporation of fractional-order derivatives into neural networks presents a novel approach to improving gradient flow and adaptive learning dynamics. This paper introduces a fractional-order LSTM model, leveraging the Grünwald–Letnikov (GL) method to modify both activation functions and backpr...

Полное описание

Библиографические подробности

Главные авторы:	Zbigniew Gomolka, Ewa Zeslawska, Lukasz Olbrot, Michal Komsa, Adrian Ćwiąkała
Формат:	Artigo
Язык:	Inglês
Опубликовано:	MDPI AG 2025-12-01
Серии:	Applied Sciences
Предметы:	fractional derivatives LSTM networks Grünwald–Letnikov method anomaly detection adaptive neuron modeling
Online-ссылка:	https://www.mdpi.com/2076-3417/15/24/13046
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