Paper Info
Title
Design of a robust interval-valued type-2 fuzzy c-regression model for a nonlinear system with noise and outliers
Abstract
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 for the handling of the uncertainties of the FCRM due to its fixed fuzzier parameter m. In the other hand, the dataset is subject to various sources of uncertainty such as measurement uncertainty, fuzziness of information and environmental noise. As a result, obtaining a high-quality approximation of real processes is often a difficult task. In this paper, the structure of the proposed clustering algorithm is given and its parameter update rule is derived. First, the modified objective functions use a kernel measure of error to deal with the noisy data. Then, a credibility function is integrated into the clustering process in order to reduce the effect of outliers. Finally, the effectiveness of the proposed algorithm is evaluated by comparing the obtained results with others reported in the literature and also through the simulation results of a real liquid level process.
Year
DOI
Venue
2019
10.1007/s00500-018-3265-z
soft computing
Keywords
Field
DocType
Type-2 fuzzy systems, Identification, Fuzzy c-regression model, Kernel approach, Noise clustering, Credibility function
Kernel (linear algebra),Mathematical optimization,Nonlinear system,Computer science,Regression analysis,Fuzzy logic,Measurement uncertainty,Outlier,Algorithm,Cluster analysis,Environmental noise
Journal
Volume
Issue
ISSN
23.0
15.0
1433-7479
Citations 
PageRank 
References 
0
0.34
33
Authors
5
Name
Order
Citations
PageRank
Moêz Soltani1285.05
Achraf Jabeur Telmoudi246.10
Lotfi Chaouech300.68
Maaruf Ali4838.82
abdelkader chaari554.56