DocumentCode
911586
Title
Learning convergence in the cerebellar model articulation controller
Author
Wong, Yiu-Fai ; Sideris, Athanasios
Author_Institution
Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA, USA
Volume
3
Issue
1
fYear
1992
fDate
1/1/1992 12:00:00 AM
Firstpage
115
Lastpage
121
Abstract
A new way to look at the learning algorithm in the cerebellar model articulation controller (CMAC) proposed by J.S. Albus (1975) is presented. A proof that the CMAC learning always converges with arbitrary accuracy on any set of training data is obtained. An alternative way to implement CMAC based on the insights obtained in the process is proposed. The scheme is tested with a computer simulation for learning the inverse dynamics of a two-link robot arm
Keywords
controllers; learning systems; neural nets; robots; CMAC learning; cerebellar model articulation controller; computer simulation; inverse dynamics; learning algorithm; training data; two-link robot arm; Computer simulation; Convergence; Helium; Memory management; Neural networks; Robots; Space technology; Table lookup; Testing; Training data;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.105424
Filename
105424
Link To Document