Broad Learning System with Preprocessing to Recover the Scattering Obstacles with Far–Field Data

Broad Learning System with Preprocessing to Recover the Scattering Obstacles with Far–Field Data

Year:    2023

Author:    Weishi Yin, Hongyu Qi, Pinchao Meng

Advances in Applied Mathematics and Mechanics, Vol. 15 (2023), Iss. 4 : pp. 984–1000

Abstract

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.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/aamm.OA-2021-0352

Advances in Applied Mathematics and Mechanics, Vol. 15 (2023), Iss. 4 : pp. 984–1000

Published online:    2023-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    17

Keywords:    Inverse scattering problem broad learning system machine learning random forest.

Author Details

Weishi Yin

Hongyu Qi

Pinchao Meng

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