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An accurate disturbance source locating method based on machine learning for complex environments

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Abstract

A method for disturbance positioning in noisy environment is introduced. By the partitioning algorithm and a well-trained B-P neural network classifier, an average locating accuracy of 95% was demonstrated in the field test.

© 2021 The Author(s)

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