Fuzzy Grammatical Inference Using Neural Network
Article Sidebar
Main Article Content
We have shown a model of fuzzy neural network that is able to infer the relations associated to the transitions of a fuzzy automaton from a fuzzy examples set. Neural network is trainned by a backpropagation of error based in a smooth derivative [Blanco96]. Once network has been trainned the fuzzy relations associated to the transitions of the automaton are found encoded in the weights.
Article Details
Com citar
Blanco Morón, Armando et al. «Fuzzy Grammatical Inference Using Neural Network». Mathware & soft computing, 1998, vol.VOL 5, núm. 2, http://raco.cat/index.php/Mathware/article/view/84740.
Articles més llegits del mateix autor/a
- José Manuel Benítez Sánchez, Armando Blanco Morón, Miguel Delgado Calvo-Flores, Ignacio Requena Ramos, Neural methods for obtaining fuzzy rules , Mathware & soft computing: 1996: Vol.: 3 Núm.: 3
- Armando Blanco Morón, Miguel Delgado Calvo-Flores, Ignacio Requena Ramos, Max-Min fuzzy neural networks for solving relational equations , Mathware & soft computing: 1994: Vol.: 1 Núm.: 3
- Miguel Delgado Calvo-Flores, Mª Carmen Pegalajar Jiménez, Manuel Pegalajar Cuéllar, Evolutionaty training for dynamical recurrent neural networks: an application in finantial time series prediction , Mathware & soft computing: 2006: Vol.: 13 Núm.: 2
- José Manuel Benítez Sánchez, Armando Blanco Morón, Miguel Delgado Calvo-Flores, Ignacio Requena Ramos, New aspects on extraction of fuzzy rules using neural networks , Mathware & soft computing: 1998: Vol.: 5 Núm.: 2-3