New Approaches to Identification of PWARX Systems
Joint Authors
Abderrahim, Kamel
Lassoued, Zeineb
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-11
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
We consider the clustering-based procedures for the identification of discrete-time hybrid systems in the piecewise affine (PWA) form.
These methods exploit three main techniques which are clustering, linear identification, and pattern recognition.
The clustering method based on the k-means algorithm is treated in this paper.
It consists in estimating both the parameter vector of each submodel and the coefficients of each partition while knowing the model orders na and nb and the number of submodels s.
The performance of this approach can be threatened by the presence of outliers and poor initializations.
To overcome these problems, we propose new techniques for data classification.
The proposed techniques exploit Chiu’s clustering technique and the self-artificial Kohonen neural network approach in order to improve the performance of both the clustering and the final linear regression procedure.
Simulation results are presented to illustrate the performance of the proposed method.
American Psychological Association (APA)
Lassoued, Zeineb& Abderrahim, Kamel. 2013. New Approaches to Identification of PWARX Systems. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1010933
Modern Language Association (MLA)
Lassoued, Zeineb& Abderrahim, Kamel. New Approaches to Identification of PWARX Systems. Mathematical Problems in Engineering No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1010933
American Medical Association (AMA)
Lassoued, Zeineb& Abderrahim, Kamel. New Approaches to Identification of PWARX Systems. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1010933
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1010933