Date of Award
Summer 1999
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical & Computer Engineering
Program/Concentration
Electrical Engineering
Committee Director
Oscar R. Gonzalez
Committee Member
Stephen A. Zahorian
Committee Member
Steven Gray
Call Number for Print
Special Collections LD4331.E55 G37
Abstract
This paper shows that the combination of a second-order neural network parameter update algorithm and internal network feedback can be effectively used for adaptive, nonlinear, dynamical system identification and control. Adaptive neural identification and control algorithms are typically utilized for real-time applications where the rate of adaptation is often critical. A fast, adaptive network parameter update algorithm is presented.
Simulation results show that this algorithm is capable of quickly identifying and adapting to changes in system parameters, making it feasible to use for real-time control and fault accommodation applications.
Rights
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
DOI
10.25777/94tz-w855
Recommended Citation
Gates, Donald A..
"Newton Parameter Update Algorithm for Recurrent Neural Networks Applied to Adaptive System Identification and Control"
(1999). Master of Science (MS), Thesis, Electrical & Computer Engineering, Old Dominion University, DOI: 10.25777/94tz-w855
https://digitalcommons.odu.edu/ece_etds/346
Included in
Artificial Intelligence and Robotics Commons, Controls and Control Theory Commons, Dynamics and Dynamical Systems Commons, Systems and Communications Commons, Theory and Algorithms Commons