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16 September 1992 Analysis of weights in a layered network for classifying active sonar returns
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Abstract
A three-layer, feed-forward network was trained to classify the echoes of active sonar pulses impinging on a target in a test tank. The data set consisted of echoes recorded as the target was rotated 1.8 degrees per step. After training on 33 examples, at 5.4 degree increments, the network was typically able to decide with better than 90% accuracy whether an echo in the larger set was produced with an angle of incidence closer to end-fire or to broadside. Training time and the fractions of upstream and downstream weights that changed in the training process were observed as the number of hidden units was varied. The hidden layer consisted of binary (0 - 1) units and the learning algorithm was Rosenblatt's 'back-propagating error correction procedure'.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James M. Coughlin, Robert H. Baran, and Richard W. Harrison "Analysis of weights in a layered network for classifying active sonar returns", Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); https://doi.org/10.1117/12.138309
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