Analysis of weights in a layered network for classifying active sonar returns

James M. Coughlin; Robert H. Baran; Richard W. Harrison

doi:10.1117/12.138309

16 September 1992 Analysis of weights in a layered network for classifying active sonar returns

James M. Coughlin, Robert H. Baran, Richard W. Harrison

Proceedings Volume 1700, Automatic Object Recognition II; (1992) https://doi.org/10.1117/12.138309
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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'.

Citation Download Citation

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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