Adjoint-operators and non-adiabatic learning algorithms in neural networks
- 1 January 1991
- journal article
- Published by Elsevier in Applied Mathematics Letters
- Vol. 4 (2) , 69-73
- https://doi.org/10.1016/0893-9659(91)90172-r
Abstract
No abstract availableKeywords
This publication has 8 references indexed in Scilit:
- Application of adjoint operators to neural learningApplied Mathematics Letters, 1990
- A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksNeural Computation, 1989
- Learning State Space Trajectories in Recurrent Neural NetworksNeural Computation, 1989
- Neutral learning of constrained nonlinear transformationsComputer, 1989
- Convergent activation dynamics in continuous time networksNeural Networks, 1989
- Dynamics and architecture for neural computationJournal of Complexity, 1988
- Sensitivity Analysis of Two-Phase Flow ProblemsNuclear Science and Engineering, 1988
- Sensitivity theory for nonlinear systems. I. Nonlinear functional analysis approachJournal of Mathematical Physics, 1981