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http://hdl.handle.net/11375/30143
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DC Field | Value | Language |
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dc.contributor.author | Ahmed R | - |
dc.contributor.author | El Sayed M | - |
dc.contributor.author | Gadsden SA | - |
dc.contributor.author | Tjong J | - |
dc.contributor.author | Habibi S | - |
dc.date.accessioned | 2024-09-08T17:22:18Z | - |
dc.date.available | 2024-09-08T17:22:18Z | - |
dc.date.issued | 2016-04 | - |
dc.identifier.issn | 0941-0643 | - |
dc.identifier.issn | 1433-3058 | - |
dc.identifier.uri | http://hdl.handle.net/11375/30143 | - |
dc.description.abstract | A multilayered neural network is a multi-input, multi-output nonlinear system in which network weights can be trained by using parameter estimation algorithms. In this paper, a novel training method is proposed. This method is based on the relatively new smooth variable structure filter (SVSF) and is formulated for feed-forward multilayer perceptron training. The SVSF is a state and parameter estimation that is based on the sliding mode concept and works in a predictor–corrector fashion. The SVSF training performance is tested on three benchmark pattern classification problems. Furthermore, a study is presented comparing the popular back-propagation method, the extended Kalman filter, and the SVSF. | - |
dc.publisher | Springer Nature | - |
dc.rights.uri | 7 | - |
dc.subject | 46 Information and Computing Sciences | - |
dc.subject | 4611 Machine Learning | - |
dc.title | Artificial neural network training utilizing the smooth variable structure filter estimation strategy | - |
dc.type | Article | - |
dc.date.updated | 2024-09-08T17:22:18Z | - |
dc.contributor.department | Mechanical Engineering | - |
dc.rights.license | Attribution-NonCommercial-NoDerivs - CC BY-NC-ND | - |
dc.identifier.doi | https://doi.org/10.1007/s00521-015-1875-2 | - |
Appears in Collections: | Mechanical Engineering Publications |
Files in This Item:
File | Description | Size | Format | |
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014-s00521-015-1875-2.pdf | Published version | 868.24 kB | Adobe PDF | View/Open |
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