A comparison study of some PWARX system identification methods

Author

Lassoued, Zeineb ; Abderrahim, Kamel

Author_Institution

Numerical Control of Ind. Processes, Univ. of Gabes, Gabes, Tunisia

fYear

2013

fDate

11-13 Oct. 2013

Firstpage

291

Lastpage

296

Abstract

In this paper the problem of identifying PieceWise AutoRegressive eXogenous (PWARX) systems is treated. Only the clustering based methods are considered. It consists in estimating both the parameter vector of each sub-model and the coefficients of each partition while knowing the model orders and the number of sub-models. We compare the k-means based methods with two recently proposed methods: the Chiu´s clustering method and the Kohonen Neural Network based method. Simulation results are presented to illustrate the performance of the proposed methods.

Keywords

autoregressive processes; identification; pattern clustering; self-organising feature maps; Chiu clustering method; Kohonen neural network; PWARX system identification methods; clustering based methods; k-means based methods; piecewise autoregressive exogenous systems; Classification algorithms; Clustering algorithms; Equations; Neural networks; Neurons; Support vector machines; Vectors; Chiu´s clustering technique; Clustering based techniques; Hybrid systems; K-means algorithm; Kohonen neural network approach; PWARX identification;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, Control and Computing (ICSTCC), 2013 17th International Conference

Conference_Location

Sinaia

Print_ISBN

978-1-4799-2227-7

Type

conf

DOI

10.1109/ICSTCC.2013.6688975

Filename

6688975