Computational intelligent techniques for cardiovascular signal analysis
Compare the performance of the back-propagation algorithm (BP) and the new algorithm extreme learning machine (ELM) for cardiovascular signal analysis.
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Online Access: | http://hdl.handle.net/10356/4983 |
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sg-ntu-dr.10356-49832023-07-04T15:45:17Z Computational intelligent techniques for cardiovascular signal analysis Nwe Ni Saw. Saratchandran, Paramasivan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Compare the performance of the back-propagation algorithm (BP) and the new algorithm extreme learning machine (ELM) for cardiovascular signal analysis. Master of Science (Computer Control and Automation) 2008-09-17T10:02:43Z 2008-09-17T10:02:43Z 2004 2004 Thesis http://hdl.handle.net/10356/4983 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Nwe Ni Saw. Computational intelligent techniques for cardiovascular signal analysis |
description |
Compare the performance of the back-propagation algorithm (BP) and the new algorithm extreme learning machine (ELM) for cardiovascular signal analysis. |
author2 |
Saratchandran, Paramasivan |
author_facet |
Saratchandran, Paramasivan Nwe Ni Saw. |
format |
Theses and Dissertations |
author |
Nwe Ni Saw. |
author_sort |
Nwe Ni Saw. |
title |
Computational intelligent techniques for cardiovascular signal analysis |
title_short |
Computational intelligent techniques for cardiovascular signal analysis |
title_full |
Computational intelligent techniques for cardiovascular signal analysis |
title_fullStr |
Computational intelligent techniques for cardiovascular signal analysis |
title_full_unstemmed |
Computational intelligent techniques for cardiovascular signal analysis |
title_sort |
computational intelligent techniques for cardiovascular signal analysis |
publishDate |
2008 |
url |
http://hdl.handle.net/10356/4983 |
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1772826943440289792 |