Supervised Learning of RFID Sensor Model using a Mobile Robot (Contributo in atti di convegno)

Type
Label
  • Supervised Learning of RFID Sensor Model using a Mobile Robot (Contributo in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/RFID-TA.2011.6068612 (literal)
Alternative label
  • G. Cicirelli, A. Milella, D. Di Paola (2011)
    Supervised Learning of RFID Sensor Model using a Mobile Robot
    in 2011 IEEE International Conference on RFID-Technologies and Applications, RFID-TA 2011, Sitges, Barcelona, Spain, 15 - 16 September 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • G. Cicirelli, A. Milella, D. Di Paola (literal)
Pagina inizio
  • 32 (literal)
Pagina fine
  • 36 (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ISSIA-CNR (literal)
Titolo
  • Supervised Learning of RFID Sensor Model using a Mobile Robot (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 9781457700286 (literal)
Abstract
  • RFID sensor modelling has been recognized as a fundamental step towards successful application of RFID technology in mobile robotics tasks, such as localization and environment mapping. In this paper, we propose a novel approach to passive RFID modelling, using fuzzy reasoning. Specifically, the RFID sensor model is defined as a combination of an RSSI model and a Tag Detection Model, both of which are learnt based on an Adaptive Neuro Fuzzy Inference System (ANFIS). Fuzzy C-Means (FCM) algorithm is applied to automatically cluster sample data into classes and obtain initial data memberships for ANFIS initialization and training. Experimental results from tests performed in our Mobile Robotics Lab are presented, showing the effectiveness of the proposed method. (literal)
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