Design of a robust interval-valued type-2 fuzzy c-regression model for a nonlinear system with noise and outliers

This paper presents the development of a novel interval-valued type-2 robust fuzzy c-regression model (IVT2RFCRM) clustering algorithm for identification of nonlinear systems taking into account the presence of noise and outliers in the associated dataset. On the one hand, the proposed method allows...

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Bibliographic Details
Published in:	Soft computing (Berlin, Germany) Vol. 23; no. 15; pp. 6125 - 6134
Main Authors:	Soltani, Moez, Telmoudi, Achraf Jabeur, Chaouech, Lotfi, Ali, Maaruf, Chaari, Abdelkader
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2019 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Background noise Clustering Computational Intelligence Control Datasets Engineering Error analysis Fuzzy sets Liquid levels Mathematical Logic and Foundations Mechatronics Methodologies and Application Nonlinear systems Optimization techniques Outliers (statistics) Parameter estimation Parameter modification Regression models Robotics Robustness (mathematics) Signal processing Uncertainty Type-2 fuzzy systems Noise clustering Credibility function Identification Kernel approach Fuzzy c-regression model
ISSN:	1432-7643, 1433-7479
Online Access:	Get full text
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