Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142444
Title: Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems
Authors: Eyoh, Imo
John, Robert
De Maere, Geert
Kayacan, Erdal
Keywords: Engineering::Mechanical engineering
Issue Date: 2018
Source: 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. doi:10.1109/TFUZZ.2018.2803751
Journal: IEEE Transactions on Fuzzy Systems
Abstract: This paper presents a novel application of a hybrid learning approach to the optimisation of membership and nonmembership 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 decoupled 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 among 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.
URI: https://hdl.handle.net/10356/142444
ISSN: 1063-6706
DOI: 10.1109/TFUZZ.2018.2803751
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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