Three-term fuzzy back-propagation
The disadvantages of the fuzzy BP learning are its low speed of error convergence and the high possibility of trapping into local minima. In this paper, a fuzzy proportional factor is added to the fuzzy BP’s iteration scheme to enhance the convergence speed. The added factor makes the proposed metho...
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2009
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my.utm.147952017-02-05T00:44:22Z http://eprints.utm.my/id/eprint/14795/ Three-term fuzzy back-propagation Mashinchi, M. Hadi Shamsuddin, Siti Mariyam T Technology (General) The disadvantages of the fuzzy BP learning are its low speed of error convergence and the high possibility of trapping into local minima. In this paper, a fuzzy proportional factor is added to the fuzzy BP’s iteration scheme to enhance the convergence speed. The added factor makes the proposed method more dependant on the distance of actual outputs and desired ones. Thus in contrast with the conventional fuzzy BP, when the slop of error function is very close to zero, the algorithm does not necessarily return almost the same weights for the next iteration. According to the simulation’s results, the proposed method is superior to the fuzzy BP in terms of generated error. Springer 2009 Book Section PeerReviewed Mashinchi, M. Hadi and Shamsuddin, Siti Mariyam (2009) Three-term fuzzy back-propagation. In: Foundations of Computational Intelligence Volume 1: Learning and Approximation. Studies in Computational Intelligence . Springer, Berlin/ Heidelberg, pp. 143-158. ISBN 978-3-642-01081-1 http://dx.doi.org/10.1007/978-3-642-01082-8_6 10.1007/978-3-642-01082-8_6 |
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T Technology (General) Mashinchi, M. Hadi Shamsuddin, Siti Mariyam Three-term fuzzy back-propagation |
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The disadvantages of the fuzzy BP learning are its low speed of error convergence and the high possibility of trapping into local minima. In this paper, a fuzzy proportional factor is added to the fuzzy BP’s iteration scheme to enhance the convergence speed. The added factor makes the proposed method more dependant on the distance of actual outputs and desired ones. Thus in contrast with the conventional fuzzy BP, when the slop of error function is very close to zero, the algorithm does not necessarily return almost the same weights for the next iteration. According to the simulation’s results, the proposed method is superior to the fuzzy BP in terms of generated error. |
| format |
Book Section |
| author |
Mashinchi, M. Hadi Shamsuddin, Siti Mariyam |
| author_facet |
Mashinchi, M. Hadi Shamsuddin, Siti Mariyam |
| author_sort |
Mashinchi, M. Hadi |
| title |
Three-term fuzzy back-propagation |
| title_short |
Three-term fuzzy back-propagation |
| title_full |
Three-term fuzzy back-propagation |
| title_fullStr |
Three-term fuzzy back-propagation |
| title_full_unstemmed |
Three-term fuzzy back-propagation |
| title_sort |
three-term fuzzy back-propagation |
| publisher |
Springer |
| publishDate |
2009 |
| url |
http://eprints.utm.my/id/eprint/14795/ http://dx.doi.org/10.1007/978-3-642-01082-8_6 |
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