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.

LITERATURE UPDATE May 20, 2021 - May 26, 2021 Literature search terms: biomech* & locomot* Publications are classified by BiomchBERT, a neural network trained on past Biomch-L Literature Updates. BiomchBERT is managed by Ryan Alcantara, a PhD Candidate at the University of Colorado Boulder. Each publication has a score (out of 100%) reflecting how confident BiomchBERT is that the publication belongs in a particular category (top 2 shown). Risteski P, Jagrić M, Pavin N, Tolić IM, Current biology: CB. (76.3% CELLULAR/SUBCELLULAR; 4.7% MUSCLE) Physical analysis reveals distinct responses of human bronchial epithelial cells to guanidine and isothiazolinone biocides. Kwon TY, Jeong J, Park E, Cho Y, Lim D, Ko UH, Shin JH, Choi J, Toxicology and applied pharmacology.