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Design of Takagi Sugeno Kang Type Interval Type-2 Fuzzy Logic Systems Optimized with Hybrid Algorithms

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Abstract

The paper designs a type of Takagi–Sugeno–Kang (TSK) type interval type-2 fuzzy logic systems for permanent magnetic drive (PMD) coercivity and maximum energy product (MEP) forecasting. The antecedents and input measurements of interval type-2 fuzzy logic systems (IT2 FLSs) are selected as Gaussian IT2 membership function (MFs) with uncertain standard deviations. The back propagation (BP) algorithms are adopted for tuning the parameters of antecedent and input measurement. Meanwhile, the recursive least square (RLS) algorithms are adopted for tuning the parameters of consequent. Monte Carlo computer simulation examples are provided to illustrate the effective of hybrid optimized IT2 FLSs in contrast to two types of type-1 (T1) FLSs.

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Acknowledgements

The paper is partially sponsored by the National Natural Science Foundation of China (61773188, 61973146), the Doctoral Start-up Foundation of Liaoning Province (2021-BS-258), and the Youth Fund of Education Department of Liaoning Province (LJKQZ2021143). The author is grateful to the well-known scholar Jerry Mendel, who has given some invaluable advices.

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  1. College of Science, Liaoning University of Technology, Jinzhou, 121001, China

    Yang Chen, Jiaxiu Yang & Chenxi Li

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  1. Yang Chen
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Correspondence to Yang Chen.

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Chen, Y., Yang, J. & Li, C. Design of Takagi Sugeno Kang Type Interval Type-2 Fuzzy Logic Systems Optimized with Hybrid Algorithms. Int. J. Fuzzy Syst. 25, 868–879 (2023). https://doi.org/10.1007/s40815-022-01410-z

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