• Robust agglomerative clusterin...

Result: Robust agglomerative clustering algorithm for fuzzy modeling purposes

Title:
Robust agglomerative clustering algorithm for fuzzy modeling purposes
Authors:
GRISALES, Victor H, SORIANO, José J, BARATO, Sergio, GONZALEZ, Diana M
Source:
Proceedings of the 2004 American Control Conference ACC (June 30-July 2, 2004, Boston Massachusetts). :1782-1787
Publisher Information:
Evanston IL; Piscataway NJ: American Automatic Control Council, IEEE, 2004.
Publication Year:
2004
Physical Description:
print, 10 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Automatique théorique. Systèmes, Control theory. Systems, Modélisation et identification, Modelling and identification, Ajustement modèle, Model matching, Ajustamiento modelo, Algorithme compétitif, Competitive algorithms, Algorithme flou, Fuzzy algorithm, Algoritmo borroso, Algorithme parallèle, Parallel algorithm, Algoritmo paralelo, Analyse régression, Regression analysis, Análisis regresión, Analyse statistique, Statistical analysis, Análisis estadístico, Classification, Clasificación, Commande non linéaire, Non linear control, Control no lineal, Identification système, System identification, Identificación sistema, Logique floue, Fuzzy logic, Lógica difusa, Mesure automatique, Automatic measurement, Medición automática, Modèle régression, Regression model, Modelo regresión, Modélisation, Modeling, Modelización, Système dynamique, Dynamical system, Sistema dinámico
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Laboratorio de Automática, Microelectrónic e Inteligencia Computacional - LAMIC Universidad Distrital FJDC Cra. 8 No. 40-62 P7, Bogotá, Colombia
Groupe Diagnostic, Supervision et Conduite qualitatifs - DISCO Laboratoire d'Analyse et d'Architecture des Systèmes LAAS-CNRS 7 Av. du Colonel Roche, 31077 Toulouse, France
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18351690
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18351690
Database:
PASCAL Archive

Further Information

This paper addresses Takagi-Sugeno-Kang (TSK) fuzzy model identification. An enhanced algorithm that uses clustering techniques for the approximation of nonlinear systems from data is presented. The algorithm combines the parallel axis version of the Gustafson-Kessel (GK) algorithm with the Fuzzy C-Regression Models (FCRM) in order to maintain the interpretability and improve the global accuracy of the model. A low sensibility to noise and automatic detection of the number of clusters is achieved by using robust statistic and competitive agglomeration techniques similar to the techniques developed in the Robust Competitive Agglomeration (RCA) algorithm. Finally, two numeric examples concerning to static and dynamic nonlinear systems are shown to demonstrate the effectiveness of the proposed algorithm.