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Identification of Sound for Pass-by Noise Test in Vehicles Using Generalized Gaussian Radial Basis Function Neural Networks

Authors

Dolores Redel-Macias, Maria , FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Jose Cubero-Atienza, Antonio , Hervas-Martinez, Cesar

External publication

Si

Means

Advances In Intelligent And Soft Computing

Scope

Proceedings Paper

Nature

Científica

JCR Quartile

✗

SJR Quartile

✗

Publication date

01/01/2011

ISI

000292618100029

Abstract

The sound of road vehicles plays a major role in providing quiet and comfortable rides. Automotive companies have invested a great deal over the last few decades to achieve this goal and attract customers. Engine noise has become one of the major sources of passenger car noise today and the demand for accurate prediction models is high. The purpose of this paper is to develop a novel noise prediction model in vehicles using a Pass-by noise test based on Artificial Neural Networks at high frequencies. The artificial neural network used in the experiments was the Generalized Gaussian Radial Basis Function Neural Network (GRBFNN). This type of RBF can reproduce different RBFs by updating a real tau parameter and allowing different shapes of RBFs in the same Neural Network. At low frequencies the system behaves linearly and therefore the proposed method improves the accuracy of the system in frequencies over 2.5 kH, obtaining a Mean Squared Error (MSE) of 0.018 +/- 3 x 10(-4), enough for our noise prediction aim.