Adaptive Stochastic Conjugate Gradient Optimization for Backpropagation Neural Networks

Backpropagation neural networks are commonly utilized to solve complicated issues in various disciplines. However, optimizing their settings remains a significant task. Traditional gradient-based optimization methods, such as stochastic gradient descent (SGD), often exhibit slow convergence and hype...

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Detalles Bibliográficos

Principais autores:	Ibrahim Abaker Targio Hashem, Fadele Ayotunde Alaba, Muhammad Haruna Jumare, Ashraf Osman Ibrahim, Anas Waleed Abulfaraj
Formato:	Artigo
Idioma:	Inglês
Publicado:	IEEE 2024-01-01
Series:	IEEE Access
Assuntos:	Adaptive stochastic conjugate gradient backpropagation neural networks stochastic gradient descent
Acceso en liña:	https://ieeexplore.ieee.org/document/10445451/
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