Self-organizing neuro-fuzzy inference system

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations


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.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings
Number of pages8
StatePublished - 2008
Externally publishedYes
Event13th Iberoamerican Congress on Pattern Recognition, CIARP 2008 - Havana, Cuba
Duration: 9 Sep 200812 Sep 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5197 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th Iberoamerican Congress on Pattern Recognition, CIARP 2008


  • Flexible architecture
  • Nonlinear modeling


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