DocumentCode :
3783091
Title :
Global parameter identification in systems with a sigmoidal activation function
Author :
A. Kojic;A.M. Annaswamy
Author_Institution :
Dept. of Mech. Eng., MIT, Cambridge, MA, USA
Volume :
2
fYear :
2000
Firstpage :
934
Abstract :
Parameter identification in a 2-node network with sigmoidal activation functions is considered. Given the nonlinearity in the weights, standard estimation algorithms based on linear parametrization are inadequate tools for studying global parameter convergence. In this paper, we provide an alternative approach for studying parameter identification in the presence of sigmoidal parametrization. Conditions under which a simple back propagation algorithm can lead to global convergence are considered.
Keywords :
"Parameter estimation","Neural networks","Convergence","Stability","Power engineering and energy","Adaptive control","Mechanical engineering","Control systems","Systems engineering and theory","Numerical simulation"
Publisher :
ieee
Conference_Titel :
American Control Conference, 2000. Proceedings of the 2000
ISSN :
0743-1619
Print_ISBN :
0-7803-5519-9
Type :
conf
DOI :
10.1109/ACC.2000.876637
Filename :
876637
Link To Document :
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