| dc.contributor.author | Benítez Sánchez, José Manuel |
| dc.contributor.author | Blanco Morón, Armando |
| dc.contributor.author | Delgado Calvo-Flores, Miguel |
| dc.contributor.author | Requena Ramos, Ignacio |
| dc.date.accessioned | 2007-09-13T09:36:27Z |
| dc.date.available | 2007-09-13T09:36:27Z |
| dc.date.issued | 1996 |
| dc.identifier.citation | Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica |
| dc.identifier.issn | 1134-5632 |
| dc.identifier.uri | http://hdl.handle.net/2099/3475 |
| dc.description.abstract | In previous papers, we presented an empirical methodology based on
Neural Networks for obtaining fuzzy rules which allow a system to be
described, using a set of examples with the corresponding inputs and
outputs. Now that the previous results have been completed, we present
another procedure for obtaining fuzzy rules, also based on Neural Networks
with Backpropagation, with no need to establish beforehand the labels or
values of the variables that govern the system |
| dc.format.extent | 371-382 |
| dc.language.iso | eng |
| dc.publisher | Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica |
| dc.relation.ispartof | Mathware & soft computing . 1996 Vol. 3 Núm. 3 |
| dc.rights | Reconeixement-NoComercial-CompartirIgual 3.0 Espanya |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
| dc.subject.other | Obtaining fuzzy rules |
| dc.subject.other | Learning |
| dc.subject.other | Artificial neural networks |
| dc.title | Neural methods for obtaining fuzzy rules |
| dc.type | Article |
| dc.subject.lemac | Lògica matemàtica |
| dc.description.peerreviewed | Peer Reviewed |
| dc.subject.ams | Classificació AMS:: 03 Mathematical logic and foundations:: 03B General logic |
| dc.rights.access | Open Access |
| local.personalitzacitacio | true |
