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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1741

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dc.contributor.authorBehera, L-
dc.contributor.authorGopal, M-
dc.contributor.authorChaudhury, S-
dc.date.accessioned2006-06-27T05:41:47Z-
dc.date.available2006-06-27T05:41:47Z-
dc.date.issued1995-
dc.identifier.citationIndustrial Automation and Control, IEEE/IAS International Conference on, 457 - 460p.en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/1741-
dc.description.abstractFast and accurate trajectory tracking of a robot arm primarily depends on the knowledge of its explicit inverse dynamics model. Online learning of inverse dynamics using a supervised learning algorithm is difficult in the absence of a priori knowledge of command error. On the other hand, a self-organizing neural network employing an unsupervised learning scheme does not depend on the command error. These networks are suitable for both off-line and online schemes of learning the inverse dynamics. The present paper proposes two schemes based on unsupervised learning algorithms, namely, Kohonen's self-organizing topology conserving feature map and “neural-gas” algorithm. Simulation results on a single link manipulator confirms the efficacy of the proposed schemesen
dc.format.extent58159 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.subjectself-organizing mapen
dc.subjectKohonen's self-organizing topology conserving feature mapen
dc.subjectneural-gas algorithmen
dc.subjectrobot controlen
dc.titleSelf-organizing neural networks for learning inverse dynamics of robot manipulatoren
dc.typeArticleen
Appears in Collections:Electrical Engineering

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