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

• Muslim M. Aziz

• Ishikawa Masumi

• Furukawa Tetsuo

書誌事項

タイトル別名

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

抄録

Proposed is a new approach to task segmentation in a mobile robot by a modular network SOM (mnSOM). In a mobile robot, however, 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.

収録刊行物

• Neural Computing and Applications

  Neural Computing and Applications 16 (6), 571-580, 2007-10-01

  Springer London

キーワード

• task segmentation
• mobile robot
• subsequence
• modular network SOM

詳細情報

• CRID: 1571135652605653376
• NII論文ID: 120002441381
• NII書誌ID: AA11006092
• ISSN: 09410643
• Web Site: http://hdl.handle.net/10228/2373
• 本文言語コード: en
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