Adaptive Stochastic Conjugate Gradient Optimization for Backpropagation Neural Networks

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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Bibliographic Details
Main Authors: Ibrahim Abaker Targio Hashem, Fadele Ayotunde Alaba, Muhammad Haruna Jumare, Ashraf Osman Ibrahim, Anas Waleed Abulfaraj
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Adaptive stochastic conjugate gradient
backpropagation
neural networks
stochastic gradient descent
Online Access:https://ieeexplore.ieee.org/document/10445451/

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