Application of adjoint operators to neural learning

DOI10.1016/0893-9659(90)90127-WzbMath0704.92003OpenAlexW2052774684WikidataQ114953330 ScholiaQ114953330MaRDI QIDQ917462

Elsie Sterbin Gottlieb

Publication date: 1990

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

Full work available at URL: https://doi.org/10.1016/0893-9659(90)90127-w

zbMATH Keywords

adjoint operatorsfast global computationneural learning of nonlinear mappingsresponse to perturbations

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Applications of operator theory in chemistry and life sciences (47N60)

Related Items (4)

Training spatially homogeneous fully recurrent neural networks in eigenvalue space. ⋮ A VECTOR MATRIX REAL TIME RECURSIVE BACKPROPAGATION ALGORITHM FOR RECURRENT NEURAL NETWORKS THAT APPROXIMATE MULTI-VALUED PERIODIC FUNCTIONS ⋮ Adjoint-operators and non-adiabatic learning algorithms in neural networks ⋮ Terminal repeller unconstrained subenergy tunneling (TRUST) for fast global optimization

Cites Work

• Adjoint-operators and non-adiabatic learning algorithms in neural networks
• Dynamics and architecture for neural computation

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