Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using Deep Learning

1 code implementation • 19 Apr 2024 • Ata Koklu, Yusuf Guven, Tufan Kumbasar

Type-1 and Interval Type-2 (IT2) Fuzzy Logic Systems (FLS) excel in handling uncertainty alongside their parsimonious rule-based structure.

Enhancing Interval Type-2 Fuzzy Logic Systems: Learning for Precision and Prediction Intervals

no code implementations • 19 Apr 2024 • Ata Koklu, Yusuf Guven, Tufan Kumbasar

In this context, we first provide extra design flexibility to the Karnik-Mendel (KM) and Nie-Tan (NT) center of sets calculation methods to increase their flexibility for generating PIs.

Prediction Intervals Uncertainty Quantification

Zadeh's Type-2 Fuzzy Logic Systems: Precision and High-Quality Prediction Intervals

no code implementations • 19 Apr 2024 • Yusuf Guven, Ata Koklu, Tufan Kumbasar

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.

Prediction Intervals Uncertainty Quantification