| 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 |
