Zadeh's Type-2 Fuzzy Logic Systems: Precision and High-Quality Prediction Intervals
<|reference_start|>Zadeh's Type-2 Fuzzy Logic Systems: Precision and High-Quality Prediction Intervals: General Type-2 (GT2) Fuzzy Logic Systems (FLSs) are perfect candidates to quantify uncertainty, which is crucial for informed decisions in high-risk tasks, as they are powerful tools in representing uncertainty. In this paper, we travel back in time to provide a new look at GT2-FLSs by adopting Zadeh's (Z) GT2 Fuzzy Set (FS) definition, intending to learn GT2-FLSs that are capable of achieving reliable High-Quality Prediction Intervals (HQ-PI) alongside precision. By integrating Z-GT2-FS with the \(\alpha\)-plane representation, we show that the design flexibility of GT2-FLS is increased as it takes away the dependency of the secondary membership function from the primary membership function. After detailing the construction of Z-GT2-FLSs, we provide solutions to challenges while learning from high-dimensional data: the curse of dimensionality, and integrating Deep Learning (DL) optimizers. We develop a DL framework for learning dual-focused Z-GT2-FLSs with high performances. Our study includes statistical analyses, highlighting that the Z-GT2-FLS not only exhibits high-precision performance but also produces HQ-PIs in comparison to its GT2 and IT2 fuzzy counterparts which have more learnable parameters. The results show that the Z-GT2-FLS has a huge potential in uncertainty quantification.<|reference_end|>
@article{guven2024zadeh's,
    title={Zadeh's Type-2 Fuzzy Logic Systems: Precision and High-Quality
  Prediction Intervals},
    author={Yusuf Guven, Ata Koklu, Tufan Kumbasar},
    journal={arXiv preprint arXiv:2404.12800},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12800},
    primaryClass={cs.LG cs.AI}
}