Kaydedildi:
| Asıl Yazarlar: | , , |
|---|---|
| Materyal Türü: | Preprint |
| Baskı/Yayın Bilgisi: |
2026
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| Konular: | |
| Online Erişim: | https://arxiv.org/abs/2602.08050 |
| Etiketler: |
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İçindekiler:
- 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.