Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/30143
Title: | Artificial neural network training utilizing the smooth variable structure filter estimation strategy |
Authors: | Ahmed R El Sayed M Gadsden SA Tjong J Habibi S |
Department: | Mechanical Engineering |
Keywords: | 46 Information and Computing Sciences;4611 Machine Learning |
Publication Date: | Apr-2016 |
Publisher: | Springer Nature |
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. |
metadata.dc.rights.license: | Attribution-NonCommercial-NoDerivs - CC BY-NC-ND |
URI: | http://hdl.handle.net/11375/30143 |
metadata.dc.identifier.doi: | https://doi.org/10.1007/s00521-015-1875-2 |
ISSN: | 0941-0643 1433-3058 |
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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