Mechanical system modelling using recurrent neural networks via quasi- Newton learning methods

DOI10.1016/0307-904X(95)00015-CzbMath0834.68097MaRDI QIDQ1900577

C. James Li, Lilai Yan

Publication date: 31 October 1995

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

zbMATH Keywords

learning algorithmsBoolean functionsrecurrent neural networksquasi- Newton methods

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Lyapunov stability analysis of gradient descent-learning algorithm in network training ⋮ Automatic structure and parameter training methods for modeling of mechanical systems by recurrent neural networks.

Cites Work

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• A new approach to variable metric algorithms
• The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations
• Conditioning of Quasi-Newton Methods for Function Minimization
• Approximation by superpositions of a sigmoidal function

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