A learning architecture for control based on back-propagation neural networks
- 1 January 1988
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 587-594 vol.2
- https://doi.org/10.1109/icnn.1988.23975
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
This publication has 2 references indexed in Scilit:
- Building and Understanding Adaptive Systems: A Statistical/Numerical Approach to Factory Automation and Brain ResearchIEEE Transactions on Systems, Man, and Cybernetics, 1987
- Learning representations by back-propagating errorsNature, 1986