Skip to main content

Research Repository

Advanced Search

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

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

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


Imo Eyoh

Robert John


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.


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.

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 Nov 21, 2018
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
Keywords Interval type-2 A-intuitionistic fuzzy logic system;Regression problems; Gradient descent algorithm
Public URL
Publisher URL
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.


You might also like

Downloadable Citations