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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Abid, S. Mouelhi, A. Fnaiech, F. |
| Copyright Year | 2006 |
| Description | Author affiliation: ESSTT, Tunis (Abid, S.; Mouelhi, A.; Fnaiech, F.) |
| Abstract | In this paper, the Davidon Fletcher Powell (DFP) algorithm for nonlinear least squares is proposed to train multilayer perceptron (MLP). Applied on both a single output layer perceptron and MLP, we find that this algorithm is faster than the Marquardt-Levenberg (ML) algorithm known as the fastest algorithm used to train MLP until now. The number of iterations required by DFP algorithm to converge is less than about 50% of what is required by the ML algorithm. Interpretations of these results are provided in the paper. |
| Starting Page | 3389 |
| Ending Page | 3394 |
| File Size | 310856 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780394909 |
| DOI | 10.1109/IJCNN.2006.247340 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-07-16 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Acceleration Multilayer perceptrons Signal processing algorithms Backpropagation algorithms Optimization methods Convergence Newton method Least squares methods Gradient methods Least squares approximation Davidon Fletcher Powell (DFP) algorithm Multilayer Perceptron (MLP) Marquardt-Levenberg (ML) algorithm |
| Content Type | Text |
| Resource Type | Article |
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