PWARX Model Identification Based on Clustering Approach

This chapter addresses the problem of clustering based procedure for the identification of PieceWise Auto-Regressive eXogenous (PWARX) models. In order to overcome the main drawbacks of the existing methods such as their sensitivity to poor initializations and the existence of outliers, we propose the use of the Chiu’s clustering algorithm and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. A comparative study of the two proposed approaches with the k-means method is achieved in simulation. The results of experimental validation are also presented to illustrate the effectiveness of the proposed methods.

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