Enhanced Concepts and Methods for Representing Real-LifeLogical Correctness in Fuzzy Frameworks

Resumen

This paper develops a unified framework for representing real-life logical correctness in soft com-
puting. We introduce eight “fitting” operators—Scope-, Prerequisite-, Counterparty-, Evidence-, Capability-,
Deadline-, Emotion-, Think-, and Context-Fitting—for both fuzzy and neutrosophic sets. Each operator updates
memberships via t-norm/t-conorm aggregation and residuated implication, yielding affine rules that preserve
range, ensure monotonicity with respect to supportive and adverse factors, and recover the prior in neutral con-
ditions. We provide formal definitions, propositions, and proofs, together with numerical case studies illustrating
parameter effects. The approach subsumes classical models and interfaces with plithogenic constructs, enabling
transparent, auditable adjustments of truth, indeterminacy, and falsity for decision support, evaluation, and
knowledge representation.