Artificial neural network training utilizing the smooth variable structure filter estimation strategy |
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Authors: | Ryan Ahmed Mohammed El Sayed S Andrew Gadsden Jimi Tjong Saeid Habibi |
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Affiliation: | 1.Department of Mechanical Engineering,McMaster University,Hamilton,Canada;2.Department of Mechanical Engineering,University of Maryland, Baltimore County,Baltimore,USA;3.Ford Canada,Windsor,Canada |
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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. |
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