Enhancing extreme learning machines using cross-entropy moth-flame optimization algorithm
Extreme Learning Machines (ELM) learn fast and eliminate the tuning of input weights and biases. However, ELM does not guarantee the optimal setting of the weights and biases due to random input parameters initialization. Therefore, ELM suffers from instability of output, large network size, and deg...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Centre for Environment and Socio-Economic Research Publications
2022
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/98680/ http://www.ceser.in/ceserp/index.php/ijai/article/view/6857 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Teknologi Malaysia |