Adjoint-operators and non-adiabatic learning algorithms in neural networks
type: Item
instance of: scholarly article
title: Adjoint-operators and non-adiabatic learning algorithms in neural networks
zbMATH Open document ID: 0727.92001
DOI: 10.1016/0893-9659(91)90172-R
author: N. Toomarian
author: J. Barhen
published in: Applied Mathematics Letters
publication date: 1991-01-01T00:00:00Z
review text: Various schemes for temporal learning have been proposed. Their drawbacks usually comprise repeated solutions of (weights, gains) gradients, storing of a large amount of history data or adiabatic assumptions. This paper proposes a new rigorous methodology enabling the implementation in a highly efficient manner. It combines the advantage of dramatic complexity reductions inherent in adjoint methods with the ability to solve the equations forward in time without adiabatic assumptions, so that real-time applications are possible. All involved mathematics is described, while no particular application is demonstrated.
reviewed by: J.Horejs
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 4202074
zbMATH Keywords: nonadiabatic learning algorithms
zbMATH Keywords: adjoint sensitivity equations
zbMATH Keywords: nonlinear neural network
zbMATH Keywords: forward in time
zbMATH Keywords: temporal learning
zbMATH Keywords: complexity reductions
zbMATH Keywords: real-time applications
Wikidata QID: Q114953329
MaRDI profile type: Publication
cites work: Application of adjoint operators to neural learning
cites work: Dynamics and architecture for neural computation
cites work: Q3494713
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description: scientific article; zbMATH DE number 4202074