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

Eyoh, Imo, John, Robert and de Maere, Geert (2017) Interval type-2 A-intuitionistic fuzzy logic for regression problems. IEEE Transactions on Fuzzy Systems . ISSN 1941-0034

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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.

Item Type: Article
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Keywords: Interval type-2 A-intuitionistic fuzzy logic system; Regression problems; Gradient descent algorithm
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
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Depositing User: Eprints, Support
Date Deposited: 10 Nov 2017 09:16
Last Modified: 04 May 2020 19:18

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