DocumentCode :
1373034
Title :
Neural networks for control theory and practice
Author :
Narendra, Kumpati S.
Author_Institution :
Center for Syst. Sci., Yale Univ., New Haven, CT, USA
Volume :
84
Issue :
10
fYear :
1996
fDate :
10/1/1996 12:00:00 AM
Firstpage :
1385
Lastpage :
1406
Abstract :
The past five years have witnessed a great deal of progress in both the theory and the practice of control using neural net works. After a long period of experimentation and research neural network-based controllers are finally emerging in the marketplace and the benefits of such controllers are now being realized in a wide variety of fields. The practical applications are also calling for a better understanding of the theoretical principles involved. In this paper we review the current status of control practice using neural networks and the theory related to it and attempt to assess the advantages of neurocontrol for technology
Keywords :
autoregressive moving average processes; feedforward neural nets; identification; neurocontrollers; nonlinear dynamical systems; NARMA models; RBF neural networks; control theory; identification; multilayer neural networks; neurocontrol; nonlinear dynamical systems; Application software; Artificial neural networks; Control systems; Control theory; Function approximation; Mathematics; Neural networks; Process control; Production; Robust stability;
fLanguage :
English
Journal_Title :
Proceedings of the IEEE
Publisher :
ieee
ISSN :
0018-9219
Type :
jour
DOI :
10.1109/5.537106
Filename :
537106
Link To Document :
بازگشت