fuzzy-logic

This paper introduces an optimized fuzzy logic approach combined with the IEEE Key Gas Method for diagnosing power transformer faults using dissolved gas analysis, achieving up to 98.6% accuracy in experimental validation.

iFuzz-Meta is an interpretable fuzzy learning framework that combines top-down knowledge integration with bottom-up data-driven adaptation to enhance transparency and generalization in neural models.

This paper introduces an expert-guided neuro-symbolic pipeline combining LLMs for semantic normalization and fuzzy logic to assess compliance with sepsis treatment protocols, providing graded insights from clinical data.

This paper presents a novel fuzzy-logic-based extension of Answer Set Programming (ASP) that integrates numerical information with qualitative reasoning using membership functions, demonstrated through a travel recommendation case study.

This paper introduces a parameter-neutral replacement for transformer feed-forward layers using explicit fuzzy set operations and quantifiers over sequences. The approach achieves comparable perplexity to GELU baselines while enabling interpretable grammatical-licensing detectors, though full Boolean FFNs remain unstable.

This paper argues that probability theory is a historically evolving form of rationality, tracing its development from combinatorial games to Bayesian inference and contrasting it with fuzzy logic and deep learning.

This paper develops a unified account of mediative fuzzy logic from its type-1 foundations through type-2, type-3, and quantum extensions, establishing soundness, paraconsistency, and conservativity, with an autonomous-braking sensor-fusion example.