Comparative of Genetic Fuzzy regression techniques for aeroacoustic phenomenons
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 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.
Hugo Henry、Kelly Cohen
航空航天技术自动化基础理论自动化技术、自动化技术设备
Hugo Henry,Kelly Cohen.Comparative of Genetic Fuzzy regression techniques for aeroacoustic phenomenons[EB/OL].(2025-05-29)[2025-06-16].https://arxiv.org/abs/2505.23746.点此复制
评论