动态更新模糊规则，让系统自适应非平稳环境。
From Model-Based and Adaptive Control to Evolving Fuzzy Control
- 从数据流中增量构建并调整模糊规则
- 可应对环境变化，适合动态场景
- 适合关注可解释性与安全的工业应用
演化模糊系统通过数据流增量式更新其规则结构，构建和自适应模糊模型——如预测器和控制器。在模糊集理论60周年之际，本文回顾了经典模糊与自适应建模控制的发展历程，重点强调演化智能系统在模糊建模与控制中的兴起及其意义，突出其在处理非平稳环境中的优势。同时探讨了安全性、可解释性及有原则的结构演化等关键挑战与未来方向。
原文摘要 · Abstract (English)
Evolving fuzzy systems build and adapt fuzzy models - such as predictors and controllers - by incrementally updating their rule-base structure from data streams. On the occasion of the 60-year anniversary of fuzzy set theory, commemorated during the Fuzz-IEEE 2025 event, this brief paper revisits the historical development and core contributions of classical fuzzy and adaptive modeling and control frameworks. It then highlights the emergence and significance of evolving intelligent systems in fuzzy modeling and control, emphasizing their advantages in handling nonstationary environments. Key challenges and future directions are discussed, including safety, interpretability, and principled structural evolution.
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