Airfoil Self Noise Prediction Using Linear Regression Approach

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)

Abstract

This project attempts to predict the scaled sound pressure levels in decibels, based on the aerodynamic and acoustic related attributes. Each attribute can be regarded as a potential feature. The problem is how to predict the sound pressure level accurately based on those features. This paper describes the approaches of using linear regression models and other optimization algorithms used for the better predictions. The comparative results and analysis are also provided in experiment and results section.

Keywords

Linear regression Airfoil Gradient descent Stochastic gradient descent Linear least squares 

References

  1. 1.
    De Jesús Rubio Avila, J., Ramírez, A.F., Flores, G.D., Pereyra, M.S., Posada, F.B.S.: The wind turbine. In: Proceedings of the 12th WSEAS International Conference on Computers, ICCOMP’08, pp. 607–615. USA (2008)Google Scholar
  2. 2.
    Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference and prediction, 2n edn. Springer, Heidelberg (2008)Google Scholar
  3. 3.
    John, A.: Ekaterinaris and Nikolaos Kampanis: a numerical prediction of acoustic fields generated by wind turbines. Syst. Anal. Model. Simul. 39(1), 49–73 (2000)MATHGoogle Scholar
  4. 4.
    Guarnaccia, C., Mastorakis, N.E., Quartieri, J.: A mathematical approach for wind turbine noise propagation. In: Proceedings of the 2011 American Conference on Applied Mathematics and the 5th WSEAS International Conference on Computer Engineering and Applications, pp. 187–194, USA (2011)Google Scholar
  5. 5.
    Coppi, R., D’Urso, P., Giordani, P., Santoro, A.: Least squares estimation of a linear regression model with LR fuzzy response. Comput. Stat. Data Anal. 51(1), 267–286 (2006)CrossRefMathSciNetMATHGoogle Scholar
  6. 6.
    Chiang, H.W.D., Fleeter, S.: Prediction of loaded airfoil unsteady aerodynamic gust response by a locally analytical method. Math. Comput. Model. 10(3), 193–206 (1988)CrossRefMATHGoogle Scholar
  7. 7.
    Bache, K., Lichman, M.: UCI Machine Learning Repository (2013)Google Scholar
  8. 8.
    Doel, K., Ascher, U.: The chaotic nature of faster gradient descent methods. J. Sci. Comput. 51(3), 560–581 (2012)CrossRefMathSciNetMATHGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Amrita Center for Cyber SecurityAmrita Vishwa VidyapeethamKollamIndia
  2. 2.Department of Computer ScienceAmrita Vishwa VidyapeethamMysoreIndia

Personalised recommendations