DocumentCode
3335523
Title
A learning architecture for control based on back-propagation neural networks
Author
Elsley, Richard K.
Author_Institution
Rockwell Int. Sci. Center, Thousand Oaks, CA, USA
fYear
1988
fDate
24-27 July 1988
Firstpage
587
Abstract
A neural-network-based control architecture has been developed which can autonomously learn to perform kinematic control of an unknown system and/or adapt to a system which changes over time. It can control continuous-valued system variables to arbitrary accuracy using a small number of neurons. It learns to control the system more accurately than an analytically calculated controller. It is fault-tolerant in the presence of a large number (e.g., 30%) of component failures. The architecture has been used to learn to control a simulated robot arm of initially unknown characteristics. The simulations run in near real time.<>
Keywords
adaptive control; fault tolerant computing; learning systems; neural nets; robots; adaptive control; back-propagation neural networks; component failures; continuous-valued system variables; fault tolerance; kinematic control; learning architecture; near real time; simulated robot arm; Adaptive control; Computer fault tolerance; Learning systems; Neural networks; Robots;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1988., IEEE International Conference on
Conference_Location
San Diego, CA, USA
Type
conf
DOI
10.1109/ICNN.1988.23975
Filename
23975
Link To Document