Author

Haoran Pei

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#artificial intelligence Feb 2026

KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning

KANFIS employs an additive aggregation mechanism, under which both model parameters and rule complexity scale linearly with input dimensionality rather than exponentially, resulting in an intrinsically interpretable model with clear rule semantics and transparent inference processes.

Bin-Bin Yong, Haoran Pei, Jun Shen et al. · arXiv.org · 3 citations

Preprint Aug 2026

HyperANFIS: Enhancing Rule Representation and Interpretability in Adaptive Neuro-Fuzzy Systems via Hyperbolic Geometry

Hyperbolic ANFIS (HyperANFIS), a hyperbolic extension of ANFIS that preserves the fuzzy semantics and core architecture of conventional ANFIS while performing rule-prototype learning, rule activation, and consequent aggregation in hyperbolic space, strengthens the fuzzy inference process.

Haoran Pei, Zhao Su, Ze-Tao Lin et al. · 0 citations