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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...

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Principais autores: Zbigniew Gomolka, Ewa Zeslawska, Lukasz Olbrot, Michal Komsa, Adrian Ćwiąkała
Format: Artigo
Sprog:Inglês
Udgivet: MDPI AG 2025-12-01
Serier:Applied Sciences
Fag:
fractional derivatives
LSTM networks
Grünwald–Letnikov method
anomaly detection
adaptive neuron modeling
Online adgang:https://www.mdpi.com/2076-3417/15/24/13046
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