Boundedness and convergence analysis of weight elimination for cyclic training of neural networks

DOI10.1016/j.neunet.2016.06.005zbMath1428.68260OpenAlexW2473976235WikidataQ45953016 ScholiaQ45953016MaRDI QIDQ2281677

Zhenyun Ye, Jacek M. Zurada, Wei-Feng Gao, Ji'an Wang

Publication date: 6 January 2020

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2016.06.005

zbMATH Keywords

convergenceboundednessneural networksweight decayweight elimination

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Hopf bifurcation analysis of a tumor virotherapy model with two time delays

Cites Work

• Convergence analysis of online gradient method for BP neural networks
• The weight decay backpropagation for generalizations with missing values
• Computational properties and convergence analysis of BPNN for cyclic and almost cyclic learning with penalty
• A Novel Recurrent Neural Network with Finite-Time Convergence for Linear Programming
• Gradient Convergence in Gradient methods with Errors
• Learning representations by back-propagating errors
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item