可解释的模糊系统用于反渗透脱盐
Interpretable Fuzzy Systems For Forward Osmosis Desalination
AI总结：
本文提出一种人机协同的模糊系统方法，用于可解释地预测反渗透脱盐生产力，通过网格划分、特征工程和规则修剪提升可解释性和预测性能。
摘要：
在水处理中，保持模糊规则基于系统（FRBS）的可解释性至关重要，因为决策影响公共健康。尽管已使用多目标算法解决结构性可解释性问题，但语义可解释性常因低可区分性的模糊集而受损。我们提出了一种人机协同的方法，用于开发可解释的FRBS以预测反渗透脱盐生产力。我们的方法整合了专家驱动的网格划分以生成可区分的隶属函数，领域引导的特征工程以减少冗余，并基于触发强度的规则修剪。该方法在预测性能上与基于聚类的FRBS相当，同时保持了语义可解释性和满足结构性复杂度约束，为水处理应用提供了可解释的解决方案。
英文摘要：
Preserving interpretability in fuzzy rule-based systems (FRBS) is vital for water treatment, where decisions impact public health. While structural interpretability has been addressed using multi-objective algorithms, semantic interpretability often suffers due to fuzzy sets with low distinguishability. We propose a human-in-the-loop approach for developing interpretable FRBS to predict forward osmosis desalination productivity. Our method integrates expert-driven grid partitioning for distinguishable membership functions, domain-guided feature engineering to reduce redundancy, and rule pruning based on firing strength. This approach achieved comparable predictive performance to cluster-based FRBS while maintaining semantic interpretability and meeting structural complexity constraints, providing an explainable solution for water treatment applications.
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