Weak and strong convergence analysis of Elman neural networks via weight decay regularization

DOI10.1080/02331934.2022.2057852OpenAlexW4226208947MaRDI QIDQ6051200

No author found.

Publication date: 19 September 2023

Published in: Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331934.2022.2057852

zbMATH Keywords

convergencegradient method\(L_2\)-regularizationElman neural networks

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Applications of mathematical programming (90C90)

Cites Work

• Unnamed Item
• Solubility and stability of recurrent neural networks with nonlinearity or time-varying delays
• Convergence of gradient method for Eelman networks
• Estimating the dimension of a model
• Improved extension neural network and its applications
• Deterministic convergence analysis via smoothing group Lasso regularization and adaptive momentum for Sigma-Pi-Sigma neural network
• Design and analysis of a novel chaotic diagonal recurrent neural network
• A reliable small sample classification algorithm by Elman neural network based on PLS and GA
• Regression Shrinkage and Selection via The Lasso: A Retrospective
• Sparse Approximate Solutions to Linear Systems
• An iterative viscosity approximation method for the split common fixed-point problem
• Learning representations by back-propagating errors

This page was built for publication: Weak and strong convergence analysis of Elman neural networks via weight decay regularization