Abstract
A self-organizing feature map (SOFM), a kind of artificial neural network (ANN) architecture, is used in this work for continental shelf seafloor sediment classification. Echo data are acquired using an echosounding system from three types of seafloor sediment areas. Excellent classification (/spl sim/100%) for an ideal output neuron grid size of 15/spl times/1 is obtained for a moving average of 35 input snapshots.
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