Conditioned adaptive behavior from Kalman filter trained recurrent networks

We demonstrate that a fixed-weight neural network can be trained with Kalman filter methods to exhibit input-output behavior that depends on which of two conditioning tasks had been performed a substantial number of time steps in the past. This behavior can also be made to survive an intervening int...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the International Joint Conference on Neural Networks, 2003 Vol. 4; pp. 3017 - 3021 vol.4
Main Authors Feldkamp, L.A., Prokhorov, D.V., Feldkamp, T.M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2003
Subjects
Adaptive systems
Chaos
Control systems
Interference
Neural networks
Recurrent neural networks
Online AccessGet full text

Cover

More Information
Summary:We demonstrate that a fixed-weight neural network can be trained with Kalman filter methods to exhibit input-output behavior that depends on which of two conditioning tasks had been performed a substantial number of time steps in the past. This behavior can also be made to survive an intervening interference task.
ISBN:9780780378988
0780378989
ISSN:1098-7576
1558-3902
DOI:10.1109/IJCNN.2003.1224052