DocumentCode
3316839
Title
A complex EKF-RTRL neural network
Author
Coelho, Pedro Henrique Gouvêa
Author_Institution
Dept. of Electron. & Telecommun., State Univ. of Rio de Janeiro, Brazil
Volume
1
fYear
2001
fDate
2001
Firstpage
120
Abstract
The purpose of the paper is to use extended Kalman filter (EKF) techniques in the complex real time recurrent learning (RTRL) neural network in order to have faster convergence as an alternative to standard gradient methods, usually used in RTRL neural networks training which are known to be slow. This characteristic is desired in many engineering applications, particularly those which are carried out online
Keywords
Kalman filters; convergence; learning (artificial intelligence); recurrent neural nets; complex real time recurrent learning neural network; convergence; extended Kalman filter techniques; neural net training; Adaptive equalizers; Convergence; Feedback loop; Gradient methods; Neural networks; Neurons; Nonlinear equations; Recurrent neural networks; State-space methods; Telecommunication standards;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.939003
Filename
939003
Link To Document