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Distending Function-based Data-Driven Type2 Fuzzy Inference System

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Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1 (FTC 2022 2022)

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Abstract

Some challenges arise when applying the existing fuzzy type2 modeling techniques. A large number of rules are required to complete cover the whole input space. A large of parameters associated with type2 membership functions have to be determined. The identified fuzzy model is usually difficult to interpret due to the large number of rules. Designing a fuzzy type2 controller using these models is a computationally expensive task. To overcome these limitations, a procedure is proposed here to identify the fuzzy type2 model directly from the data. This model is called the Distending Function-based Fuzzy Inference System (DFIS). The proposed procedure is used to model the altitude controller of a quadcopter. The DFIS model performance is compared with various fuzzy models. The performance of this controller is compared with type1 and type2 fuzzy controllers.

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Acknowledgment

The research was supported by the Ministry of Innovation and Technology NRDI Office (Project no. TKP2021-NVA-09) within the framework of the Artificial Intelligence National Laboratory Program (RRF-2.3.1-21-2022-00004).

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Authors and Affiliations

  1. Institute of Informatics, University of Szeged, 6720, Szeged, Hungary

    József Dombi & Abrar Hussain

Authors
  1. József Dombi
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  2. Abrar Hussain
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Correspondence to Abrar Hussain .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Dombi, J., Hussain, A. (2023). Distending Function-based Data-Driven Type2 Fuzzy Inference System. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_47

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