A comparison of nonlinear optimization methods for supervised learning in multilayer feedforward neural networks

DOI10.1016/0377-2217(96)00035-5zbMath0912.90253OpenAlexW1982057193MaRDI QIDQ1268172

Ming S. Hung, James W. Denton

Publication date: 18 October 1998

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0377-2217(96)00035-5

zbMATH Keywords

neural networkspattern classificationsupervised learningtrainingmultilayer feedforward networks

Mathematics Subject Classification ID

Nonlinear programming (90C30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (3)

Multilayer neural networks: an experimental evaluation of on-line training methods ⋮ TRANSFORM-ANN for online optimization of complex industrial processes: casting process as case study ⋮ A recursive algorithm for nonlinear least-squares problems

Uses Software

• L-BFGS

Cites Work

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• Updating Quasi-Newton Matrices with Limited Storage
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• Function minimization by conjugate gradients
• Convergence Conditions for Ascent Methods
• A Family of Variable-Metric Methods Derived by Variational Means
• A new approach to variable metric algorithms
• On Steepest Descent
• The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations
• Conditioning of Quasi-Newton Methods for Function Minimization

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