DocumentCode
1929731
Title
Conditioned adaptive behavior from Kalman filter trained recurrent networks
Author
Feldkamp, Lee A. ; Prokhorov, Danil V. ; Feldkamp, Timothy M.
Author_Institution
Res. & Adv. Eng., Ford Motor Co., Dearborn, MI, USA
Volume
4
fYear
2003
fDate
20-24 July 2003
Firstpage
3017
Abstract
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.
Keywords
Kalman filters; learning (artificial intelligence); recurrent neural nets; Kalman filter trained recurrent networks; conditioned adaptive behavior; fixed-weight neural network; input output behavior; intervening interference task; Adaptive systems; Chaos; Control systems; Interference; Neural networks; Recurrent neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1224052
Filename
1224052
Link To Document