Robust interval type-2 FCRM algorithm for nonlinear systems identification in a stochastic environment

This paper investigates the sensibility of the interval type-2 fuzzy c-regression algorithm to noise and outliers. To overcome this problem, a modified version of this algorithm is presented. The consequences parameters of local models are estimated using the weighted recursive least squares method....

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Veröffentlicht in:	2017 International Conference on Control, Automation and Diagnosis (ICCAD) S. 180 - 184
Hauptverfasser:	Sameh, Khadhraoui, Chaari, Abdelkader
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.01.2017
Schlagworte:	Algorithm design and analysis Clustering algorithms interval type-2 fuzzy c-regression models algorithm Linear programming Mathematical model Noise measurement noisy clustering algorithm outliers Partitioning algorithms Robustness Type-2 fuzzy sets weighted recursive least square
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