Asymptotic convergence of feedback error learning method and improvement of learning speed
- 30 December 2002
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 761-767 vol.2
- https://doi.org/10.1109/iros.1993.583156
Abstract
Deals with the improvement of learning speed based on the analysis of the convergence of the feedback error learning method. The authors derive the condition for the asymptotic convergence of the feedback error learning method for each trial. This condition is the relationship between the learning rate and the /spl alpha/ function, which is calculated from the input-output relationship of the system. Using the /spl alpha/ function obtained above, a high-speed learning method for a trajectory control system is obtained. Simulations results are given for the trajectory control of a 1 link robot manipulator in two cases: (1) using a general feedback error learning method and (2) using the proposed high-speed learning method. The simulation results show the effectiveness of the proposed conditions and learning method.Keywords
This publication has 6 references indexed in Scilit:
- Learning control using neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Theory and applications of neural networks for industrial control systemsIEEE Transactions on Industrial Electronics, 1992
- Hierarchical Control System For Flexible Materials Handling Robots Using Neural NetworkPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Learning control for a closed loop system using feedback-error-learningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990
- Generic constraints on underspecified target trajectoriesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- A hierarchical neural-network model for control and learning of voluntary movementBiological Cybernetics, 1987