| Título |
Fuzzy modeling from black-box data with deep learning techniques |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
14th International Symposium on Neural Networks, ISNN 2017 |
| Resumen |
Deep learning techniques have been successfully used for pattern classification. These advantage methods are still not applied in fuzzy modeling. In this paper, a novel data-driven fuzzy modeling approach is proposed. The deep learning methods is applied to learn the probability properties of input and output pairs. We propose special unsupervised learning methods for these two deep learning models with input data. The fuzzy rules are extracted from these properties. These deep learning based fuzzy modeling algorithms are validated with three benchmark examples. © Springer International Publishing AG 2017. |
| Observaciones |
DOI 10.1007/978-3-319-59072-1_36
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10261 |
| Lugar |
Hokkaido |
| País |
Japon |
| No. de páginas |
304-312 |
| Vol. / Cap. |
10261 LNCS |
| Inicio |
2017-06-21 |
| Fin |
2017-06-26 |
| ISBN/ISSN |
9783319590714 |
