NDLI: Optimal unsupervised motor learning for dimensionality reduction of nonlinear control systems

Content Provider	IEEE Xplore Digital Library
Author	Sanger, T.D.
Copyright Year	1990
Abstract	In this paper, optimal unsupervised motor learning is defined to be a technique for finding the coordinate system of minimum dimensionality which can adequately describe a particular motor task. An explicit method is provided for learning a stable controller that translates commands within the new coordinate system into motor variables appropriate for plant control. The method makes use of previously described neural network algorithms including the generalized Hebbian algorithm, basis-function trees, and trajectory extension learning. Examples of applications to a real direct-drive two joint planar robot arm and a simulated three joint robot arm with visual sensing are given.<>
Sponsorship	IEEE Computational Intelligence Society
Starting Page	965
Ending Page	973
Page Count	9
File Size	975820
File Format	PDF
ISSN	10459227
Volume Number	5
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear control systems Control systems Backpropagation algorithms Robot sensing systems Robot kinematics Unsupervised learning Sensor systems Bandwidth Neural networks Supervised learning
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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