Adjoint-operators and non-adiabatic learning algorithms in neural networks

DOI10.1016/0893-9659(91)90172-RzbMath0727.92001WikidataQ114953329 ScholiaQ114953329MaRDI QIDQ804485

N. Toomarian, Jacob Barhen

Publication date: 1991

Published in: Applied Mathematics Letters (Search for Journal in Brave)

zbMATH Keywords

adjoint sensitivity equationscomplexity reductionsforward in timenonadiabatic learning algorithmsnonlinear neural networkreal-time applicationstemporal learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (4)

Classification of temporal trajectories by continuous-time recurrent nets ⋮ Identification of nonlinear dynamics using a general-spatio-temporal network ⋮ Application of adjoint operators to neural learning ⋮ Adjoint-operators and non-adiabatic learning algorithms in neural networks

Cites Work

• Adjoint-operators and non-adiabatic learning algorithms in neural networks
• Application of adjoint operators to neural learning
• Dynamics and architecture for neural computation
• Unnamed Item

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