Classification of Underwater Acoustic Transients by Artificial Neural Networks.

摘要

Artificial neural networks have been trained using the backpropagation algorithm to classify a variety of model transient source signals. The networks were then tested on signals propagated to 25 different receiver sites by the time-domain parabolic equation model. Despite the interference effects from surface and bottom reflections, the classification accuracy is about 90% in the noise-free case, virtually identical to that of a nearest-neighbor classifier on the same problem. Classification in the presence of noise is considerably reduced; however, the redundancy provided by the multiple receivers in most cases allows the network to correctly classify all signals from sources on which it was trained. In addition, it shows a robustness in the presence of unknown signals not shown by the nearest-neighbor classifier.