DocumentCode :
1819058
Title :
Diagonal recurrent neural networks for nonlinear system control
Author :
Ku, Chao-Chee ; Lee, Kwang Y.
Author_Institution :
Dept. of Electr. & Comput. Eng., Pennsylvania State Univ., University Park, PA, USA
Volume :
1
fYear :
1992
fDate :
7-11 Jun 1992
Firstpage :
315
Abstract :
The authors present an approach for control and system identification using diagonal recurrent neural networks (DRNNs). An unknown plant is identified by a system identifier, called a diagonal recurrent neuroidentifier (DRNI), and provides information on the plant to a controller, called a diagonal recurrent neurocontroller (DRNC). A generalized algorithm, called the dynamic backpropagation algorithm, is developed to train both the DRNC and the DRNI. The DRNN captures the dynamic nature of a system and, since it is not fully connected, training is much faster than with a fully connected recurrent neural network
Keywords :
backpropagation; neural nets; nonlinear control systems; diagonal recurrent neural networks; diagonal recurrent neurocontroller; diagonal recurrent neuroidentifier; dynamic backpropagation algorithm; system identification; Backpropagation algorithms; Control systems; Heuristic algorithms; Neural networks; Neurocontrollers; Neurons; Nonlinear control systems; Nonlinear systems; Recurrent neural networks; System identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
Type :
conf
DOI :
10.1109/IJCNN.1992.287192
Filename :
287192
Link To Document :
بازگشت