用遗传模糊系统建模机翼自噪声，提升航空噪音预测精度。
Comparative of Genetic Fuzzy regression techniques for aeroacoustic phenomenons
- 结合遗传算法与模糊推理，构建三类回归模型。
- 聚类辅助的模糊系统在保持精度下显著降低复杂度。
- 适合航空航天、汽车及无人机领域的噪声预测研究者。
本研究探讨了遗传模糊系统（GFS）在建模机翼自噪声中的应用，这是航空声学中一个关键问题，对航空航天、汽车和无人机领域具有重要意义。利用公开的Airfoil Self Noise数据集，比较了多种模糊回归策略：高规则密度的暴力式Takagi-Sugeno-Kang（TSK）模糊系统、级联遗传模糊树（GFT）架构，以及基于模糊C均值（FCM）的新颖聚类方法，以降低模型复杂度。结果表明，聚类辅助的模糊推理在保持性能的同时有效简化了模型结构，验证了其在复杂气动声学现象建模中的有效性。
原文摘要 · Abstract (English)
This study investigates the application of Genetic Fuzzy Systems (GFS) to model the self-noise generated by airfoils, a key issue in aeroaccoustics with significant implications for aerospace, automotive and drone applications. Using the publicly available Airfoil Self Noise dataset, various Fuzzy regression strategies are explored and compared. The paper evaluates a brute force Takagi Sugeno Kang (TSK) fuzzy system with high rule density, a cascading Geneti Fuzzy Tree (GFT) architecture and a novel clustered approach based on Fuzzy C-means (FCM) to reduce the model's complexity. This highlights the viability of clustering assisted fuzzy inference as an effective regression tool for complex aero accoustic phenomena. Keywords : Fuzzy logic, Regression, Cascading systems, Clustering and AI.
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