| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 409562 | Neurocomputing | 2006 | 4 Pages |
Abstract
This Letter presents a simple and powerful pruning method for multilayer feed forward neural networks based on the fuzzy sigmoid activation function presented in [E. Soria, J. Martín, G. Camps, A. Serrano, J. Calpe, L. Gómez, A low-complexity fuzzy activation function for artificial neural networks, IEEE Trans. Neural Networks 14(6) (2003) 1576–1579]. Successful performance is obtained in standard function approximation and channel equalization problems. Pruning allows to reduce network complexity considerably, achieving a similar performance to that obtained by unpruned networks.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
E. Soria-Olivas, J.D. Martín-Guerrero, A.J. Serrano-López, J. Calpe-Maravilla, J. Vila-Francés, G. Camps-Valls,