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dc.contributor.authorAnam, Khairul
dc.contributor.authorAl-Jumaily, Adel
dc.date.accessioned2018-04-04T03:11:51Z
dc.date.available2018-04-04T03:11:51Z
dc.date.issued2018-04-04
dc.identifier.isbn978-3-319-12636-4
dc.identifier.urihttp://repository.unej.ac.id/handle/123456789/85193
dc.description21st International Conference, ICONIP 2014 Kuching, Malaysia, November 3–6, 2014 Proceedings, Part Ien_US
dc.description.abstractThis paper proposes a new structure of wavelet extreme learning machine i.e. an adaptive wavelet extreme learning machine (AW-ELM) for finger motion recognition using only two EMG channels. The adaptation mechanism is performed by adjusting the wavelet shape based on the input information. The performance of the proposed method is compared to ELM using wavelet (W-ELM0 and sigmoid (Sig-ELM) activation function. The experimental results demonstrate that the proposed AW-ELM performs better than W-ELM and Sig-ELM.en_US
dc.language.isoenen_US
dc.subjectWavelet extreme learning machineen_US
dc.subjectadaptiveen_US
dc.titleAdaptive Wavelet Extreme Learning Machine (AW-ELM) for Index Finger Recognition Using Two-Channel Electromyographyen_US
dc.typeProsidingen_US


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