An application of Pi-Sigma network in underwater acoustic objects classification

For a long time, the classification of underwater acoustic objects has been a very difficult problem to be solved, because it is affected by many factors. Now, with the development of technique of artificial neural network (ANN), a lot of researchers have devoted to the classification subject using several of kinds of ANNs. In the paper, we first introduce the Pi-Sigma network (PSN), a higher order ANN, research it's two learning algorithms based respectively on the gradient descent method and the conjugate gradient method, and then use it for objects radiated-noise classification. Comparing with multiple layer perceptron (MLP) network, Pi-Sigma network has some advantages such as simple structure, rapid convergence speed and small storage needed and so on. The inputs of the Pi-Sigma net classifier are the feature vectors which are extracted by a constant Q bandpass filter bank. The classification results on the realistic data, which are noises radiated by different class objects, demonstrate the feasibility of the classification system consisted of a constant Q bandpass filter bank as a feature extractor and a Pi-Sigma net as a classifier, achieving a statisfactory classification accuracy:>=95%.