Adaptive Wavelet Extreme Learning Machine  (AW-ELM) for Index Finger Recognition   Using Two-Channel Electromyography

Please use this identifier to cite or link to this item: https://repository.unej.ac.id/xmlui/handle/123456789/85193

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DC Field	Value	Language
dc.contributor.author	Anam, Khairul	-
dc.contributor.author	Al-Jumaily, Adel	-
dc.date.accessioned	2018-04-04T03:11:51Z	-
dc.date.available	2018-04-04T03:11:51Z	-
dc.date.issued	2018-04-04	-
dc.identifier.isbn	978-3-319-12636-4	-
dc.identifier.uri	http://repository.unej.ac.id/handle/123456789/85193	-
dc.description	21st International Conference, ICONIP 2014 Kuching, Malaysia, November 3–6, 2014 Proceedings, Part I	en_US
dc.description.abstract	This 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.iso	en	en_US
dc.subject	Wavelet extreme learning machine	en_US
dc.subject	adaptive	en_US
dc.title	Adaptive Wavelet Extreme Learning Machine (AW-ELM) for Index Finger Recognition Using Two-Channel Electromyography	en_US
dc.type	Prosiding	en_US
Appears in Collections:	LSP-Conference Proceeding

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