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: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI AG
2025-12-01
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| coleção: | Applied Sciences |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/2076-3417/15/24/13046 |
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