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....

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	2017 International Conference on Control, Automation and Diagnosis (ICCAD) s. 180 - 184
Hlavní autori:	Sameh, Khadhraoui, Chaari, Abdelkader
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.01.2017
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	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. This approach is tested and validated using two examples.
DOI:	10.1109/CADIAG.2017.8075653