Show simple item record

dc.contributor.authorZalama, Eduardoen_US
dc.contributor.authorGuadiano, Paoloen_US
dc.contributor.authorLópez-Coronado, Juanen_US
dc.date.accessioned2011-11-14T18:19:26Z
dc.date.available2011-11-14T18:19:26Z
dc.date.issued1993-10
dc.identifier.urihttps://hdl.handle.net/2144/2033
dc.description.abstractThis article introduces an unsupervised neural architecture for the control of a mobile robot. The system allows incremental learning of the plant during robot operation, with robust performance despite unexpected changes of robot parameters such as wheel radius and inter-wheel distance. The model combines Vector associative Map (VAM) learning and associate learning, enabling the robot to reach targets at arbitrary distances without knowledge of the robot kinematics and without trajectory recording, but relating wheel velocities with robot movements.en_US
dc.description.sponsorshipSloan Fellowship (BR-3122); Air Force Office of Scientific Research (F49620-92-J-0499)en_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-1993-056
dc.rightsCopyright 1993 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.subjectNeural networksen_US
dc.subjectVAMen_US
dc.subjectAVITEen_US
dc.subjectCompetitive learningen_US
dc.subjectUnsuperviseden_US
dc.subjectMobile roboten_US
dc.titleUnsupervised Neural Network for the Control of a Mobile Roboten_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


This item appears in the following Collection(s)

Show simple item record