Development of an artificial neural network based noise prediction model for opencast mines

• Autores: Nanda Santosh Kumar, Debi Prasad Tripathy, Sarat Kumar Patra
• Localización: Noise Control Engineering Journal, ISSN 0736-2501, Vol. 58, Nº. 2, 2010, págs. 105-120
• Idioma: inglés
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• Resumen

  • Artificial neural network models are simple to apply and have generated a great deal of interest in engineering. By considering the successfully application of artificial neural networks in complex engineering problems, in this paper, artificial neural network system based noise prediction models were developed for predicting far field noise levels due to operation of specific set of mining machinery. Multi-Layer Perceptron (MLP) and Radial Basis Function Network (RBFN) systems were used to predict the machinery noise in opencast mines. The proposed models were designed with VDI-2714 noise prediction model. It was taken due to simplicity, for design of artificial neural network system based noise prediction models for opencast mines. From the present investigations, it was observed that the RBFN model gave better noise predictions than the MLP model.