HyperANFIS：通过双曲几何增强自适应神经模糊系统中的规则表示与可解释性
HyperANFIS: Enhancing Rule Representation and Interpretability in Adaptive Neuro-Fuzzy Systems via Hyperbolic Geometry
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中文总结 AI 辅助
本研究针对现有ANFIS模型在欧氏空间中推理导致表示能力受限的问题，提出双曲扩展模型HyperANFIS，其在双曲空间中完成规则相关操作并保留可解释性，实验显示该模型性能优于多种ANFIS变体。
中文摘要 AI 辅助
自适应神经模糊推理系统（ANFIS）是一种可解释的推理框架，能够生成显式的IF-THEN模糊规则，适用于需要透明推理的任务。然而，现有ANFIS模型通常在欧氏空间中构建规则前件并执行推理，限制了其表示能力和预测性能。为解决这一问题，我们提出Hyperbolic ANFIS（HyperANFIS），这是ANFIS的双曲扩展模型。HyperANFIS在保留传统ANFIS模糊语义和核心架构的同时，在双曲空间中执行规则原型学习、规则激活及后件聚合，且仍具备生成可解释IF-THEN规则的能力。通过利用双曲几何的表示特性，HyperANFIS强化了模糊推理过程，进而提升了预测准确率、规则间协作能力及可解释规则的可信度。实验结果表明，在所有数据集上，HyperANFIS均持续优于标准ANFIS基线及多种ANFIS变体，同时生成更高质量的模糊规则。
英文摘要
The adaptive neuro-fuzzy inference system (ANFIS) is an interpretable reasoning framework capable of generating explicit IF-THEN fuzzy rules, making it suitable for tasks requiring transparent reasoning. However, existing ANFIS models generally construct rule antecedents and perform inference in Euclidean space, limiting their representational capacity and predictive performance. To address this issue, we propose Hyperbolic ANFIS (HyperANFIS), a hyperbolic extension of ANFIS. HyperANFIS preserves the fuzzy semantics and core architecture of conventional ANFIS while performing rule-prototype learning, rule activation, and consequent aggregation in hyperbolic space. It also retains the ability to generate interpretable IF-THEN rules. By exploiting the representational properties of hyperbolic geometry, HyperANFIS strengthens the fuzzy inference process, thereby improving predictive accuracy, inter-rule collaboration, and the credibility of its interpretable rules. Experimental results show that HyperANFIS consistently outperforms the standard ANFIS baseline and various ANFIS variants across all datasets, while also generating higher-quality fuzzy rules.
