Some clustering techniques for modelling uncertain nonlinear systems

Author

Zribi, Ali ; Djemel, Mohamed ; Chtourou, Mohamed

Author_Institution

Dept. of Electr. Eng., Res. Unit on Intell. Control, Sfax

fYear

2008

fDate

20-22 July 2008

Firstpage

1

Lastpage

7

Abstract

This paper presents popular unsupervised clustering algorithms based on neuro-fuzzy, fuzzy c-means (FCM) and agglomerative techniques. The purpose of this paper is to provide clustering methods able to cluster the data patterns without a priori information about the number of clusters. We will show that it is possible to reconcile the FCM algorithm with the unsupervised clustering algorithms. Finally, to show the efficiencies of these algorithms, we will apply them to model the behaviour of uncertain system.

Keywords

fuzzy set theory; pattern clustering; agglomerative techniques; data pattern clustering; fuzzy c-mean; neuro-fuzzy; uncertain nonlinear system modelling; unsupervised clustering algorithm; Clustering algorithms; Clustering methods; Fuzzy neural networks; Fuzzy systems; Intelligent control; Neural networks; Nonlinear systems; Partitioning algorithms; Signal design; Uncertain systems; Agglomerative clustering; FCM clustering; Neuro-fuzzy clustering; unsupervised clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Signals and Devices, 2008. IEEE SSD 2008. 5th International Multi-Conference on

Conference_Location

Amman

Print_ISBN

978-1-4244-2205-0

Electronic_ISBN

978-1-4244-2206-7

Type

conf

DOI

10.1109/SSD.2008.4632878

Filename

4632878