Abstract
Prediction of cabin noise for new types of ships and offshore platforms, based on measurement or simulation databases, is a common problem that needs a solution at the beginning of the design process. In this paper, we explore the use of a radial basis function (RBF) neural network to study this problem. Within the framework of the RBF network, we implement and compare several algorithms to devise a fast and precise cabin noise prediction model. We select a combination of algorithms after training the RBF with noise measurement samples. The results show that the RBF neural network trained using the DE algorithm has better prediction accuracy, generalization, and robustness than the others. Our work provides a new method for preliminary noise assessment during the schematic design phase and enables rapid analysis of vibration and noise control schemes for ships and offshore platforms.
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