Comparative of Genetic Fuzzy regression techniques for aeroacoustic phenomenons

2025-05-29

AI Summary

This study addresses the critical aeroacoustic problem of airfoil self-noise modeling. Leveraging the publicly available Airfoil Self-Noise dataset, it systematically evaluates a genetic fuzzy regression approach. The proposed method introduces a lightweight fuzzy inference architecture: input space preprocessing via Fuzzy C-Means (FCM) clustering mitigates rule explosion, while a novel Cascaded Genetic Fuzzy Tree (GFT) framework—integrating Takagi–Sugeno–Kang (TSK) fuzzy systems with genetic algorithm optimization—is applied for the first time to aeroacoustic regression. Experimental results demonstrate that the method achieves high prediction accuracy while substantially reducing model complexity: compared to conventional TSK systems, it reduces the number of fuzzy rules by over 60% and lowers RMSE by 12.3%. These findings validate the effectiveness and practicality of the clustering-assisted cascaded fuzzy structure for aeroacoustic regression modeling.

Technology Category

Machine Learning: Unsupervised & Self-Supervised LearningCognitive Modeling & Cognitive Systems: Neural Spike CodingConstraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingSemantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Modeling airfoil self-noise using Genetic Fuzzy Systems
• Comparing Fuzzy regression strategies for aeroacoustic phenomena
• Reducing model complexity with clustering-assisted fuzzy inference

Innovation

• Genetic Fuzzy Systems model airfoil self-noise
• Compares brute force and cascading fuzzy architectures
• Uses Fuzzy C-means clustering to reduce complexity