Comparative of Genetic Fuzzy regression techniques for aeroacoustic phenomenons

TL;DR

This paper compares different Genetic Fuzzy Regression techniques for modeling aeroacoustic phenomena, demonstrating the effectiveness of clustering-based approaches in reducing model complexity and improving accuracy.

Contribution

It introduces and evaluates a novel clustered Fuzzy C-means approach within Genetic Fuzzy Systems for aeroacoustic noise modeling.

Findings

Clustering reduces model complexity effectively.

Fuzzy C-means enhances regression accuracy.

Genetic Fuzzy Systems are viable for aeroacoustic modeling.

Abstract

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.