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Enhancing Interval Type-2 Fuzzy Logic Systems: Learning for Precision and Prediction Intervals
April 22, 2024, 4:41 a.m. | Ata Koklu, Yusuf Guven, Tufan Kumbasar
cs.LG updates on arXiv.org arxiv.org
Abstract: In this paper, we tackle the task of generating Prediction Intervals (PIs) in high-risk scenarios by proposing enhancements for learning Interval Type-2 (IT2) Fuzzy Logic Systems (FLSs) to address their learning challenges. 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. These enhancements increase the flexibility of KM in the defuzzification stage while the NT in …
abstract arxiv challenges context cs.ai cs.lg design extra flexibility interval logic paper precision prediction risk systems type
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