  General Type-2 (GT2) Fuzzy Logic Systems (FLSs) are perfect candidates to
quantify uncertainty, which is crucial for informed decisions in high-risk
tasks, as they are powerful tools in representing uncertainty. In this paper,
we travel back in time to provide a new look at GT2-FLSs by adopting Zadeh's
(Z) GT2 Fuzzy Set (FS) definition, intending to learn GT2-FLSs that are capable
of achieving reliable High-Quality Prediction Intervals (HQ-PI) alongside
precision. By integrating Z-GT2-FS with the \(\alpha\)-plane representation, we
show that the design flexibility of GT2-FLS is increased as it takes away the
dependency of the secondary membership function from the primary membership
function. After detailing the construction of Z-GT2-FLSs, we provide solutions
to challenges while learning from high-dimensional data: the curse of
dimensionality, and integrating Deep Learning (DL) optimizers. We develop a DL
framework for learning dual-focused Z-GT2-FLSs with high performances. Our
study includes statistical analyses, highlighting that the Z-GT2-FLS not only
exhibits high-precision performance but also produces HQ-PIs in comparison to
its GT2 and IT2 fuzzy counterparts which have more learnable parameters. The
results show that the Z-GT2-FLS has a huge potential in uncertainty
quantification.
