Title :
Neural network system identification for improved noise rejection
Author :
Hyland, David C. ; Collins, Emmanuel G., Jr. ; Haddad, Wassim M. ; Hunter, Douglas L.
Author_Institution :
Gov. Aerosp. Syst. Div., Harris Corp., Melbourne, FL, USA
Abstract :
Neural networks are a viable paradigm for adaptive system identification and control. This paper develops adaptive neural identification algorithms that are able to minimize the influences of extrinsic noise on the quality of the identified model
Keywords :
adaptive control; adaptive systems; identification; neurocontrollers; noise; adaptive system control; adaptive system identification; extrinsic noise influence minimization; improved noise rejection; neural network system identification; Adaptive control; Current measurement; Mechanical engineering; Modeling; Neural networks; Programmable control; Sensor systems; Signal processing; System identification; Time measurement;
Conference_Titel :
American Control Conference, Proceedings of the 1995
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2445-5
DOI :
10.1109/ACC.1995.529267
