Design of a robust interval-valued type-2 fuzzy c-regression model for a nonlinear system with noise and outliers
DOI10.1007/s00500-018-3265-zOpenAlexW2805302109WikidataQ129740289 ScholiaQ129740289MaRDI QIDQ2318544
Maaruf Ali, Achraf Jabeur Telmoudi, Moêz Soltani, Lotfi Chaouech, Abdelkader Chaari
Publication date: 15 August 2019
Published in: Soft Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00500-018-3265-z
identificationcredibility functionkernel approachtype-2 fuzzy systemsnoise clusteringfuzzy c-regression model
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Fuzziness, and linear inference and regression (62J86) Multivariate analysis and fuzziness (62H86)
Related Items (5)
Cites Work
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- The concept of a linguistic variable and its application to approximate reasoning. I
- A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization
- Fuzzy identification of systems and its applications to modeling and control
- Correlation based model validity tests for non-linear models
- Nonlinear model validation using correlation tests
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