iFuzz-Meta：一种连接自上而下与自下而上知识融合的可解释模糊学习框架
iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration
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- Australian AI Institute（澳大利亚人工智能研究所）
- University of Technology Sydney（悉尼科技大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本文提出iFuzz-Meta可解释模糊学习框架，结合元学习与知识引导正则化实现知识融合，实验验证其具备可解释推理与稳定跨领域泛化能力，为可解释模糊系统提供通用路径。
中文摘要 AI 辅助
可解释表示学习是现代神经计算中的关键挑战，尤其当模型不仅需要执行任务，还需解释其推理过程时。本文提出iFuzz-Meta，一种基于模糊规则的可解释学习框架，可在现代神经架构中保留人类可理解的推理结构。每条模糊规则对应原始特征空间中定义的语义与空间原型，实现透明推理与直接可解释性。元学习被用作分析范式，以研究这些可解释规则如何跨任务与领域重组，为算法适应与认知表示的关联提供原则性手段。知识引导的正则化机制进一步实现自上而下与自下而上的融合，其中理论先验作为软归纳偏置，而数据驱动学习则对其进行细化与扩展。这一双重过程确保适应沿语义与生理意义的轨迹进行，而非任意参数偏移。评估表明，iFuzz-Meta可实现可解释推理与稳定的跨领域泛化，为构建可解释且感知知识的模糊系统建立了潜在的通用路径。
英文摘要
Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform but also to explain their reasoning. This paper introduces iFuzz-Meta, an interpretable fuzzy rule-based learning framework that preserves human-understandable reasoning structures within modern neural architectures. Each fuzzy rule corresponds to a semantic and spatial prototype defined in the original feature space, enabling transparent inference and direct interpretability. Meta-learning is employed as an analytical paradigm to examine how these interpretable rules reorganize across tasks and domains, providing a principled means to link algorithmic adaptation with cognitive representation. A knowledge-guided regularization mechanism further enables a top-down-bottom-up integration, in which theoretical priors act as soft inductive biases while data-driven learning refines and extends them. This dual process ensures that adaptation proceeds along semantically and physiologically meaningful trajectories, rather than arbitrary parameter shifts. Evaluations demonstrate that iFuzz-Meta achieves interpretable reasoning and stable cross-domain generalization, establishing a potential general pathway toward explainable and knowledge-aware fuzzy systems.
