Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems

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

Abstract

This paper presents a novel application of a hybrid learning approach to the optimisation of membership and non-membership functions of a newly developed interval type-2 intuitionistic fuzzy logic system (IT2 IFLS) of a Takagi-Sugeno-Kang (TSK) fuzzy inference system with neural network learning capability. The hybrid algorithms consisting of decou- pled extended Kalman filter (DEKF) and gradient descent (GD) are used to tune the parameters of the IT2 IFLS for the first time. The DEKF is used to tune the consequent parameters in the forward pass while the GD method is used to tune the antecedents parts during the backward pass of the hybrid learning. The hybrid algorithm is described and evaluated, prediction and identification results together with the runtime are compared with similar existing studies in the literature. Performance comparison is made between the proposed hybrid learning model of IT2 IFLS, a TSK-type-1 intuitionistic fuzzy logic system (IFLS-TSK) and a TSK-type interval type-2 fuzzy logic system (IT2 FLS-TSK) on two instances of the datasets under investigation. The empirical comparison is made on the designed systems using three artificially generated datasets and three real world datasets. Analysis of results reveal that IT2 IFLS outperforms its type-1 variants, IT2 FLS and most of the existing models in the literature. Moreover, the minimal run time of the proposed hybrid learning model for IT2 IFLS also puts this model forward as a good candidate for application in real time systems.

Citation

Eyoh, I., John, R., de Maere, G., & Kayacan, E. (2018). Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems. IEEE Transactions on Fuzzy Systems, 26(5), 2672-2685. https://doi.org/10.1109/TFUZZ.2018.2803751

• Journal Article Type: Article
• Acceptance Date: Jan 31, 2018
• Online Publication Date: Feb 8, 2018
• Publication Date: Oct 1, 2018
• Deposit Date: Feb 2, 2018
• Publicly Available Date: Feb 8, 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: 5
• Pages: 2672-2685
• DOI: https://doi.org/10.1109/TFUZZ.2018.2803751
• Keywords: Interval type-2 intuitionistic fuzzy logic system; Decoupled extended Kalman filter; Gradient descent algorithm
• Public URL: https://nottingham-repository.worktribe.com/output/910657
• Publisher URL: http://ieeexplore.ieee.org/document/8286852/
• Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
• Contract Date: Feb 2, 2018