Ensembles of evolutionary product unit or RBF neural networks for the identification of sound for pass-by noise test in vehicles
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.
ensembles identification of sound radial basis function neural networks hybrid algorithm regression product unit neural networks evolutionary algorithms