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Interval type-2 A-intuitionistic fuzzy logic for regression problems

Eyoh, Imo; John, Robert; de Maere, Geert

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Authors

Imo Eyoh

Robert John



Abstract

This paper presents an approach to prediction based on a new interval type-2 Atanassov-intuitionistic fuzzy logic system (IT2AIFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference with neural network learning capability. The gradient descent (GD) algorithm is used to adapt the parameters of the IT2AIFLS. The empirical comparison is made on the designed system using some benchmark regression problems - both artificial and real world datasets. Analyses of our results reveal that IT2AIFLS outperforms its type-1 variant, other type-1 fuzzy logic approaches and some type-2 fuzzy systems in the regression tasks. The reason for the improved performance of the proposed framework of IT2AIFLS is because of the introduction of non-membership functions and intuitionistic fuzzy indices into the classical IT2FLS model. This increases the level of fuzziness in the proposed IT2AIFLS framework, thus providing more accurate approximations than AIFLS, classical type-1 and interval type-2 fuzzy logic systems.

Citation

Eyoh, I., John, R., & de Maere, G. (2018). Interval type-2 A-intuitionistic fuzzy logic for regression problems. IEEE Transactions on Fuzzy Systems, 26(4), 2396-2408. https://doi.org/10.1109/TFUZZ.2017.2775599

Journal Article Type Article
Acceptance Date Nov 8, 2017
Online Publication Date Nov 20, 2017
Publication Date Aug 30, 2018
Deposit Date Nov 10, 2017
Publicly Available Date Mar 29, 2024
Journal IEEE Transactions on Fuzzy Systems
Print ISSN 1063-6706
Electronic ISSN 1941-0034
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 26
Issue 4
Pages 2396-2408
DOI https://doi.org/10.1109/TFUZZ.2017.2775599
Keywords Interval type-2 A-intuitionistic fuzzy logic system;Regression problems; Gradient descent algorithm
Public URL https://nottingham-repository.worktribe.com/output/895852
Publisher URL http://ieeexplore.ieee.org/document/8115302/
Additional Information c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

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