DocumentCode
3548810
Title
Meta-Learning for Adaptive Identification of Non-Linear Dynamical Systems
Author
Oubbati, Mohamed ; Levi, Paul ; Schanz, Michael
Author_Institution
Dept. of Image Understanding, Stuttgart Univ.
fYear
2005
fDate
27-29 June 2005
Firstpage
473
Lastpage
478
Abstract
Adaptive identification of non-linear dynamical systems via recurrent neural networks (RNNs) is presented in this paper. We explore the notion that a fixed-weight RNN needs to change only its internal state to change its behavior policy. This ability is acquired through prior training procedure that enable the learning of adaptive behaviors. Some simulation results are presented
Keywords
identification; neurocontrollers; nonlinear dynamical systems; recurrent neural nets; adaptive identification; meta-learning; nonlinear dynamical system; recurrent neural network; Autoregressive processes; Control systems; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Recurrent neural networks; Switches; System identification; Velocity control;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation
Conference_Location
Limassol
ISSN
2158-9860
Print_ISBN
0-7803-8936-0
Type
conf
DOI
10.1109/.2005.1467061
Filename
1467061
Link To Document