Ensembles of evolutionary product unit or RBF neural networks for the identification of sound for pass-by noise test in vehicles

Authors:
MaríA Dolores Redel-MacíAs;Francisco FernáNdez-Navarro;Pedro Antonio GutiéRrez;Antonio José Cubero-Atienza;CéSar HerváS-MartíNez
Affiliations:
Engineering Project Area, Department of Rural Engineering, University of Córdoba, 14074 Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 3rd Floor, 14074 Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 3rd Floor, 14074 Córdoba, Spain;Engineering Project Area, Department of Rural Engineering, University of Córdoba, 14074 Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 3rd Floor, 14074 Córdoba, Spain
Venue:
Neurocomputing
Year:
2013

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Abstract

In order to ensure the success of new product developments and to study different alternatives of designs before their manufacture, it is primordial to assess identification models. This practice is an extensive one in the automotive industry. Automotive manufacturers invest a lot of effort and money to improve the vibro-acoustics performance of their products because they have to comply with the noise emission standards. International standards, commonly known as pass-by and coast-by noise test, define a procedure for measuring vehicle noise at different receptor positions. The aim of this work is to develop a novel model which can be used in pass-by noise test in vehicles based on ensembles of hybrid evolutionary product unit or radial basis function neural networks (EPUNNs or ERBFNNs) at high frequencies. Statistical models and ensembles of hybrid EPUNN and ERBFNN approaches have been used to develop different noise identification models. The results obtained using different ensembles of hybrid EPUNNs and ERBFNNs show that the functional model and the hybrid algorithms proposed provide a very accurate identification compared to other statistical methodologies used to solve this regression problem.