Characterization of inclusions in a non-homogeneous GPR problem by neural networks
نویسندگان
چکیده
This paper addresses the problem of inverting Ground Penetrating Radar (GPR) data, to find the buried inclusions characteristics depth and radii considering a nonhomogenous host media by using neural networks (NN). The aim is the detection and characterization of inclusions in concrete structures. A novel asynchronous model is proposed to the NN arrangement. The model is shown to outperform the traditional approaches of using one NN with two outputs or two parallel independent NN. Results are included to show the performance of the new model.
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