@Article{AAMM-15-4,
author = {Yin, Weishi and Qi, Hongyu and Pinchao, Meng},
title = {Broad Learning System with Preprocessing to Recover the Scattering Obstacles with Far–Field Data},
journal = {Advances in Applied Mathematics and Mechanics},
year = {2023},
volume = {15},
number = {4},
pages = {984--1000},
abstract = {<p style="text-align: justify;">Based on Broad Learning System with preprocessing, the impenetrable obstacles were reconstructed. Firstly, the far-field data were preprocessed by Random
Forest, and the shapes of the obstacles were classified by dividing the far-field data
into different categories. Secondly, the broad learning system was employed for reconstructing the unknown scatterer. The far-field data of the scatterer were regarded
as the input nodes of mapped features in the network, and all the mapped features
were connected with the enhancement nodes of random weights to the output layer.
Subsequently, the coefficient of the output can be obtained by the pseudoinverse. This
method for the recovery of the scattering obstacles is named RF-BLS. Finally, numerical experiments revealed that the proposed method is effective, and that the training
speed was significantly improved, compared with the deep learning method.</p>},
issn = {2075-1354},
doi = {https://doi.org/10.4208/aamm.OA-2021-0352},
url = {https://global-sci.com/article/72862/broad-learning-system-with-preprocessing-to-recover-the-scattering-obstacles-with-farfield-data}
}