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Neural Computing and Applications
Published in: Neural Computing and Applications 6/2007

01-10-2007 | ICONIP2006

Task segmentation in a mobile robot by mnSOM: a new approach to training expert modules

Authors: M. Aziz Muslim, Masumi Ishikawa, Tetsuo Furukawa

Published in: Neural Computing and Applications | Issue 6/2007

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Abstract

Proposed is a new approach to task segmentation in a mobile robot by a modular network SOM (mnSOM). In a mobile robot the standard mnSOM is not applicable as it is, because it is based on the assumption that class labels are known a priori. In a mobile robot, only a sequence of data without segmentation is available. Hence, we propose to decompose it into many subsequences, supposing that a class label does not change within a subsequence. Accordingly, training of mnSOM is done for each subsequence in contrast to that for each class in the standard mnSOM. The resulting mnSOM demonstrates good segmentation performance of 94.05% for a novel dataset.
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Literature
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Aziz Muslim M, Ishikawa M, Furukawa T (2006) A new approach to task segmentation in mobile robots by mnSOM. In: Proceedings of 2006 IEEE world congress on computational intelligence (IJCNN2006 Section), Vancouver, Canada, pp 6542–6549
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Metadata
Title
Task segmentation in a mobile robot by mnSOM: a new approach to training expert modules
Authors
M. Aziz Muslim
Masumi Ishikawa
Tetsuo Furukawa
Publication date
01-10-2007
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 6/2007
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-007-0109-7

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