Averaging Spectra to Improve the Classification of the Noise Radiated by Ships Using Neural Networks

William Soares-FilhoJosé Manoel de SeixasLuiz Pereira Calôba

The noise radiated from ships in the ocean contains information about their machinery, being normally used for detection and identification purposes. In this work, we use a neural classifier to identify the radiated noise received by a hydrophone that was far from the ship. The classification is performed in the frequency domain using a feedforward neural network, which is trained using the backpropagation algorithm. It is shown that the use of averaged spectral information during the production phase improves significantly the efficiency of the classifier, when it is compared to a neural classifier that processes frequency domain data obtained from individual acquisition windows.

Caso o link acima esteja inválido, faça uma busca pelo texto completo na Web: Buscar na Web

Biblioteca Digital Brasileira de Computação - Contato:
     Mantida por: