Clustering for nonlinear system identification

  • Authors:
  • José De Jesús Rubio Avila;Andrés Ferreyra Ramírez;Carlos Avilés-Cruz;Ivan Vazquez-Alvarez

  • Affiliations:
  • Departamento de Electrónica, Area de Instrumentación Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Azcapotzalco, México D. F., México;Departamento de Electrónica, Area de Instrumentación Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Azcapotzalco, México D. F., México;Departamento de Electrónica, Area de Instrumentación Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Azcapotzalco, México D. F., México;Departamento de Electrónica, Area de Instrumentación Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Azcapotzalco, México D. F., México

  • Venue:
  • ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

We propose a new on-line clustering fuzzy neural network. Learning structure and parameter learning are updated at the same time in our algorithm, we do not make difference in structure learning and parameter learning. It generates groups with a given radius. The center is updated in order to get that the center is near to the incoming data in each iteration, in this way, It does not need to generate a new rule in each iteration, i.e., it does not generate many rules and It does not need to prune the rules.