PWARX Model Identification Based on Clustering Approach - Complex System Modelling and Control Through Intelligent Soft Computations - page 189

7 Conclusion

In this chapter, we have considered only the clustering based procedures for the
identi cation of PWARX systems. We focused on the most challenging step which

is the task of data points classi

cation. In fact, we have proposed the use of two
clustering techniques which are the Chiu s clustering algorithm and the DBSCAN

algorithm. These algorithms present several advantages. Firstly, they do not require
any initialization so the problem of convergence towards local minima is overcome.
Secondly, these algorithms are able to remove the outliers from the data set. Finally,
our approaches generate automatically the number of sub-models. Numerical
simulation results are presented to demonstrate the performance of the proposed
approaches and to compare them with the k-means one. Also, an experimental
validation with an olive oil reactor is presented to illustrate the ef

'

ciency of the

developed methods.

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