Comparative of Genetic Fuzzy regression techniques for aeroacoustic phenomenons

This study investigates the application of Genetic Fuzzy Systems (GFS) to model the self-noise generated by airfoils, a key issue in aeroacoustics 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 Genetic 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-acoustic phenomena.