Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2010

Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers

Abdullah Almaksour
  • Function : Author
  • PersonId : 872000
Eric Anquetil

Abstract

We present in this paper a new method for the design of evolving neuro-fuzzy classifiers. The presented approach is based on a first-order Takagi-Sugeno neuro-fuzzy model. We propose a modification on the premise structure in this model and we provide the necessary learning formulas, with no problem-dependent parameters. We demonstrate by the experimental results the positive effect of this modification on the overall classification performance
Fichier principal
Vignette du fichier
AlmaksourICMLA10.pdf (938.5 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00763296 , version 1 (10-12-2012)

Identifiers

  • HAL Id : hal-00763296 , version 1

Cite

Abdullah Almaksour, Eric Anquetil. Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers. International Conference on Machine Learning and Applications ICMLA, 2010, washington, United States. ⟨hal-00763296⟩
205 View
131 Download

Share

Gmail Facebook X LinkedIn More