Self-organizing neuro-fuzzy inference system

Héctor Allende-Cid, Alejandro Veloz, Rodrigo Salas, Steren Chabert, Héctor Allende

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

12 Citas (Scopus)

Resumen

The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers's ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user's performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings
Páginas429-436
Número de páginas8
DOI
EstadoPublicada - 2008
Publicado de forma externa
Evento13th Iberoamerican Congress on Pattern Recognition, CIARP 2008 - Havana, Cuba
Duración: 9 sept. 200812 sept. 2008

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen5197 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia13th Iberoamerican Congress on Pattern Recognition, CIARP 2008
País/TerritorioCuba
CiudadHavana
Período9/09/0812/09/08

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