声纳目标识别的神经网络

ACM-SE 28 Pub Date : 1990-04-01 DOI:10.1145/98949.99150

Michael O'Rourke, J. Wood

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引用次数: 0

摘要

本文探讨了用反向传播算法训练的前馈神经网络在声纳目标识别领域的应用。实验中使用了来自不同目标的16个声纳波形，每个波形分别以13、10和3 db的信噪比引入噪声。用不同的噪声波形组合对网络进行训练，并用训练过程中不使用的噪声数据对网络进行测试。所有病例的分类均100%正确。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A neural network for sonar target recognition

This paper explores the area of sonar target recognition us ing a feedforward neural network trained with the backpropagation algorithm. Sixteen sonar waves from a variety of targets were used in the experiment Noise was introduced to each waveform at 13,10, and 3 db signal to noise ratio. The network was trained with different com binations of the noisy waveforms and tested with noisy data not used in the training process. 100% correct classi fication was obtained in all cases.

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来源期刊

ACM-SE 28

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