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

arXiv:2404.12792v1 Announce Type: new
Abstract: Type-1 and Interval Type-2 (IT2) Fuzzy Logic Systems (FLS) excel in handling uncertainty alongside their parsimonious rule-based structure. Yet, in learning large-scale data challenges arise, such as the curse of dimensionality and training complexity of FLSs. The complexity is due mainly to the constraints to be satisfied as the learnable parameters define FSs and the complexity of the center of the sets calculation method, especially of IT2-FLSs. This paper explicitly focuses on the learning problem …

abstract arxiv challenges complexity cs.ai cs.lg data deep learning dimensionality excel interval logic scale systems the curse of dimensionality training type uncertainty