Fuzzy rules with quantifiers as weights

• We introduce a new, simple GUHA quantifier and provide an original construction of double and equivalence quantifiers. • We embed tailored GUHA quantifiers into a novel model of weighted fuzzy rules. • We model functional dependencies using interpretable implicational rules. • We design an ASSOC-based classification framework with implicational rule models. • Our approach enhances explainability while maintaining strong predictive granularity. In this paper, we explore the use of General Unary Hypotheses Automaton quantifiers and provide representations for their specific subclasses. Furthermore, we focus explicitly on implicational quantifiers for analyzing specific relational dependencies. We discuss their suitability in fuzzy modeling and demonstrate their integration with appropriate fuzzy rules to create a new class of weighted fuzzy rules. This study contributes to the advancement of fuzzy modeling and offers a framework for further research and practical applications.