DocumentCode
1390957
Title
Adaptive reinforcement learning system for linearization control
Author
Hwang, Kao-Shing ; Chao, Horng-Jen
Author_Institution
Dept. of Electr. Eng., Nat. Chung Cheng Univ., Tainan, Taiwan
Volume
47
Issue
5
fYear
2000
fDate
10/1/2000 12:00:00 AM
Firstpage
1185
Lastpage
1188
Abstract
A linearization scheme is proposed to demonstrate how a neural network scheme learns to linearize a system without any identification. The process occurs within an evaluator and a controller, which communicate with each other through reinforcement signals. From simulation results, the proposed learning scheme notably surpasses the conventional neural network approaches
Keywords
backpropagation; control system analysis computing; linearisation techniques; neural nets; adaptive reinforcement learning system; backpropagation; control system linearisation; controller; evaluator; linearization control; neural network; nonlinear system analysis; reinforcement signals; Adaptive control; Adaptive systems; Artificial neural networks; Control systems; Learning; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Programmable control; Quantization;
fLanguage
English
Journal_Title
Industrial Electronics, IEEE Transactions on
Publisher
ieee
ISSN
0278-0046
Type
jour
DOI
10.1109/41.873231
Filename
873231
Link To Document