Comparative of Genetic Fuzzy regression techniques for aeroacoustic phenomenons
This work addresses a key problem in aeroacoustics for aerospace, automotive, and drone applications, but it is incremental as it compares existing fuzzy regression techniques on a known dataset.
The study tackled modeling airfoil self-noise using Genetic Fuzzy Systems, finding that a clustered approach based on Fuzzy C-means effectively reduced model complexity while maintaining viability for aeroacoustic regression.
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.