提出新型可解释模糊系统，线性扩展且支持不确定性建模。
KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning
- 用加法聚合机制替代乘积推理，规则数随维度线性增长
- 兼容一型与区间二型模糊系统，显式处理不确定性和模糊性
- 通过稀疏掩码生成简洁规则集，模型透明易懂
自适应神经模糊推理系统（ANFIS）旨在结合神经网络的学习能力与模糊逻辑的可解释性。然而传统ANFIS架构存在结构复杂问题，基于乘积的推理机制在高维空间导致规则数量指数级爆炸。本文提出柯尔莫戈洛夫-阿诺德神经模糊推理系统（KANFIS），一种紧凑的神经符号架构，融合模糊推理与加法函数分解。KANFIS采用加法聚合机制，使模型参数和规则复杂度随输入维度线性增长而非指数级增长。此外，该框架兼容一型（T1）与区间二型（IT2）模糊逻辑系统，支持对模糊表示中的不确定性与模糊性进行显式建模。通过稀疏掩码机制，KANFIS生成紧凑且结构化的规则集，实现内在可解释性，具有清晰的规则语义与透明的推理过程。实验结果表明，KANFIS在性能上可媲美代表性神经网络与神经模糊基线模型。
原文摘要 · Abstract (English)
Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed to combine the learning capabilities of neural network with the reasoning transparency of fuzzy logic. However, conventional ANFIS architectures suffer from structural complexity, where the product-based inference mechanism causes an exponential explosion of rules in high-dimensional spaces. We herein propose the Kolmogorov-Arnold Neuro-Fuzzy Inference System (KANFIS), a compact neuro-symbolic architecture that unifies fuzzy reasoning with additive function decomposition. KANFIS employs an additive aggregation mechanism, under which both model parameters and rule complexity scale linearly with input dimensionality rather than exponentially. Furthermore, KANFIS is compatible with both Type-1 (T1) and Interval Type-2 (IT2) fuzzy logic systems, enabling explicit modeling of uncertainty and ambiguity in fuzzy representations. By using sparse masking mechanisms, KANFIS generates compact and structured rule sets, resulting in an intrinsically interpretable model with clear rule semantics and transparent inference processes. Empirical results demonstrate that KANFIS achieves competitive performance against representative neural and neuro-fuzzy baselines.
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