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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2074/1741
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Full metadata record
| DC Field | Value | Language |
|---|
| contributor.author | Behera, L | - |
| contributor.author | Gopal, M | - |
| contributor.author | Chaudhury, S | - |
| date.accessioned | 2006-06-27T05:41:47Z | - |
| date.available | 2006-06-27T05:41:47Z | - |
| date.issued | 1995 | - |
| identifier.citation | Industrial Automation and Control, IEEE/IAS International Conference on, 457 - 460p. | en |
| identifier.uri | http://eprint.iitd.ac.in/dspace/handle/2074/1741 | - |
| description.abstract | Fast 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 schemes | en |
| format.extent | 58159 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | en |
| subject | self-organizing map | en |
| subject | Kohonen's self-organizing topology conserving feature map | en |
| subject | neural-gas algorithm | en |
| subject | robot control | en |
| title | Self-organizing neural networks for learning inverse dynamics of robot manipulator | en |
| type | Article | en |
| Appears in Collections: | Electrical Engineering
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Description |
Size | Format |
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| beherasel1995.pdf | | 56Kb | Adobe PDF | View/Open |
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