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Detection and classification of buried dielectric anomalies using a separated aperture sensor and a neural network discriminator
1992
IEEE Transactions on Instrumentation and Measurement
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The problem of detection and classification of buried dielectric anomalies using a separated aperture microwave sensor and an artificial neural network discriminator was considered. Several methods for training and data representation were developed to study the trainability and generalization capabilities of the networks. The effect of the architectural variation on the network performance was also studied. The principal component method was used to reduce the volume of the data and also the
doi:10.1109/19.126648
fatcat:lusyaqkhyrbxjnundinzsq4fxy