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Modeling of robot inverse kinematics using two ANN paradigms

The performance of two artificial neural networks, trained to learn data obtained from the kinematics model of a robotic arm, was compared. The trained artificial neural network (ANN) simulators were implemented to position the robotic manipulator demonstrating the feasibility of using ANN technolog...

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Main Authors: Yang, S.S., Moghavvemi, M., Tolman, J.D.
Format: Conference or Workshop Item
Published: Elsevier Science 2000
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/9681/
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record_format eprints
spelling my.um.eprints.96812017-11-23T01:47:10Z http://eprints.um.edu.my/9681/ Modeling of robot inverse kinematics using two ANN paradigms Yang, S.S. Moghavvemi, M. Tolman, J.D. TA Engineering (General). Civil engineering (General) The performance of two artificial neural networks, trained to learn data obtained from the kinematics model of a robotic arm, was compared. The trained artificial neural network (ANN) simulators were implemented to position the robotic manipulator demonstrating the feasibility of using ANN technology in actual implementations. Graphs were plotted to show relevant errors for robotic workspace and conclusions derived with reference to ANN's level of accuracy. Elsevier Science 2000 Conference or Workshop Item PeerReviewed Yang, S.S. and Moghavvemi, M. and Tolman, J.D. (2000) Modeling of robot inverse kinematics using two ANN paradigms. In: IEEE Region 10 Annual International Conference, Proceedings/TENCON, 24 September 2000 through 27 September 2000, Kuala Lumpur, Malaysia..
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Yang, S.S.
Moghavvemi, M.
Tolman, J.D.
Modeling of robot inverse kinematics using two ANN paradigms
description The performance of two artificial neural networks, trained to learn data obtained from the kinematics model of a robotic arm, was compared. The trained artificial neural network (ANN) simulators were implemented to position the robotic manipulator demonstrating the feasibility of using ANN technology in actual implementations. Graphs were plotted to show relevant errors for robotic workspace and conclusions derived with reference to ANN's level of accuracy.
format Conference or Workshop Item
author Yang, S.S.
Moghavvemi, M.
Tolman, J.D.
author_facet Yang, S.S.
Moghavvemi, M.
Tolman, J.D.
author_sort Yang, S.S.
title Modeling of robot inverse kinematics using two ANN paradigms
title_short Modeling of robot inverse kinematics using two ANN paradigms
title_full Modeling of robot inverse kinematics using two ANN paradigms
title_fullStr Modeling of robot inverse kinematics using two ANN paradigms
title_full_unstemmed Modeling of robot inverse kinematics using two ANN paradigms
title_sort modeling of robot inverse kinematics using two ann paradigms
publisher Elsevier Science
publishDate 2000
url http://eprints.um.edu.my/9681/
_version_ 1643688627088130048
score 13.154949

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