Title :
The Grey Support Vector Regression method in noise source identification
Author :
Yang, Yang ; Wang, Xiuqin ; Zhang, Di
Author_Institution :
Sch. of Eng., Bohai Univ., Jinzhou, China
Abstract :
Noise source identification is essential for making noise reduction strategies. This paper presents an approach to acoustic noise identification by introducing modern spectrum estimation, Grey Support Vector Regression (GSVR). Modern spectrum was used to recognize the main noise source and GSVR was used to do curve fitting to recognize the similarity among different curves of power spectrum which made the result more precise. The ranking of the noise sources was obtained on the basis of their individual contribution to the overall noise.
Keywords :
acoustic noise; noise abatement; physics computing; regression analysis; support vector machines; acoustic noise identification; curve fitting; grey support vector regression; noise reduction; noise source identification; spectrum estimation; Artificial neural networks; Kernel; Noise; Predictive models; Spectral analysis; Support vector machines; Training data; Grey Support Vector Regression (GSVR); Noise Identification; Power Spectrum;
Conference_Titel :
Control and Decision Conference (CCDC), 2011 Chinese
Conference_Location :
Mianyang
Print_ISBN :
978-1-4244-8737-0
DOI :
10.1109/CCDC.2011.5968236
