Convergent Decomposition Techniques for Training RBF Neural Networks

DOI10.1162/08997660152469396zbMath0986.68109OpenAlexW2138056536WikidataQ52056958 ScholiaQ52056958MaRDI QIDQ2746344

Luigi Grippo, Marco Sciandrone, Claudio A. Buzzi

Publication date: 10 October 2001

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

Full work available at URL: http://hdl.handle.net/2158/256061

zbMATH Keywords

globally convergent decomposition algorithms

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Global optimization issues in deep network regression: an overview, Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach, Block layer decomposition schemes for training deep neural networks, A unified convergence framework for nonmonotone inexact decomposition methods, On the convergence of a block-coordinate incremental gradient method

Cites Work

• On the convergence of the block nonlinear Gauss-Seidel method under convex constraints
• Globally convergent block-coordinate techniques for unconstrained optimization
• On search directions for minimization algorithms