PWARX Model Identification Based on Clustering Approach

Lassoued, Zeineb; Abderrahim, Kamel

doi:10.1007/978-3-319-12883-2_6

Cited by 3 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several solutions have been presented in the literature for the identification of PWA models. [25][26][27][28][29] We have advocated the use of the clustering based method, [30][31][32] which is based on the assumption that the process behaves locally affine. So, first of all, it can gather close regression vectors in local datasets and identifies an ARX model over each using least squares.…”

Section: Introductionmentioning

confidence: 99%

Extension of a clustering identification approach to multivariable piecewise affine systems: Application to an industrial dryer

Zeineb,

Kamel

2024

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

The main objective of this paper is the extension of the clustering-based identification approach to Multi-Input Multi-Output (MIMO) PieceWise Affine systems (PWA). This approach is performed by three main steps which are data clustering, parameters matrices estimation and regions computing. Data clustering is the most important step because the performances depend on the results given by the used clustering algorithm. In the case of MIMO PWA systems, we should cluster matrices of parameters which are considered high dimensional data. However, most of the conventional clustering algorithms are not efficient since the similarity assessment which is based on the distances between objects is fruitless in high dimension space. Therefore, we propose an extension of the DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering technique to identify MIMO PWA systems. The simulation results presented in this paper illustrate the performance of the proposed method. An application to an industrial dryer of Di-Calcium Phosphate (DCP) is also presented in order to strengthen the simulation results.

show abstract

Section: Introductionmentioning

confidence: 99%

Extension of a clustering identification approach to multivariable piecewise affine systems: Application to an industrial dryer

Zeineb,

Kamel

2024

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

“…There exist numerous approaches in the literature for the identification of PWA models Tian et al (2011), Ferrari-Trecate et al (2003, Bemporad et al (2003), Juloski et al (2005), Bemporad et al (2005). We have advocated the use of clustering based approach Lassoued and Abderrahim (2014c), which is based on the fact that process has local affine behaviours. So, first of all, close regression vectors are gathered in local datasets.…”

Section: Introductionmentioning

confidence: 99%

Extension of the clustering identification by extending the Density Based Spatial Clustering of Applications with Noise approach to Multi-Input Multi-Output Piece Wise Affine systems: Application to an industrial robot

Lassoued,

Abderrahim

2023

CEAI

View full text Add to dashboard Cite

In this paper the problem of clustering based identification of a Multi-Input Multi-Output (MIMO) PieceWise Affine systems (PWA) is considered. This approach, originally designed for systems with a Multiple-Input Single-Output (MISO) structure, is carried out by three main steps which are data clustering, parameters vectors estimation and regions computing. Data clustering is the most important step because the two other steps depend on the results given by the used clustering algorithm. In case of MIMO PWA systems, we should cluster matrices of parameters which are considered as high dimensional data. However, most of the conventional clustering algorithms do not work well in terms of effectiveness and efficiency since the similarity assessment which is based on the distances between objects is fruitless in high dimension space. Therefore, we propose an extension of the DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering approach for the identification of MIMO PWA systems. The simulation results presented in this paper prove the performance of the suggested approach. An application of the proposed approach to an industrial robot manipulator is then presented in order to validate the simulation results.

show abstract

“…It is impossible to exclusive clustering approaches to cluster news items into different clusters. Any other way, fuzzy clustering approaches are very complicated without any considerable improvement [10].…”

Section: Introductionmentioning

confidence: 99%

A Customized Non-Exclusive Clustering Algorithm for News Recommendation Systems

Darvishy¹,

Ibrahim²,

Sidi³

2019

JUBPAS

View full text Add to dashboard Cite

Clustering is one of the main tasks in machine learning and data mining and is being utilized in many applications including news recommendation systems. In this paper, we propose a new non-exclusive clustering algorithm named Ordered Clustering (OC) with the aim is to increase the accuracy of news recommendation for online users. The basis of OC is a new initialization technique that groups news items into clusters based on the highest similarities between news items to accommodate news nature in which a news item can belong to different categories. Hence, in OC, multiple memberships in clusters are allowed. An experiment is carried out using a real dataset which is collected from the news websites. The experimental results demonstrated that the OC outperforms the k-means algorithm with respect to Precision, Recall, and F1-Score.

show abstract

PWARX Model Identification Based on Clustering Approach

Cited by 3 publications

References 36 publications

Extension of a clustering identification approach to multivariable piecewise affine systems: Application to an industrial dryer

Extension of a clustering identification approach to multivariable piecewise affine systems: Application to an industrial dryer

Extension of the clustering identification by extending the Density Based Spatial Clustering of Applications with Noise approach to Multi-Input Multi-Output Piece Wise Affine systems: Application to an industrial robot

A Customized Non-Exclusive Clustering Algorithm for News Recommendation Systems

Contact Info

Product

Resources

About