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.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
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
-
Shape reconstruction of acoustic obstacle with linear sampling method and neural network
Tang, Bowen | Yang, Xiaoying | Su, LinAIMS Mathematics, Vol. 9 (2024), Iss. 6 P.13607
https://doi.org/10.3934/math.2024664 [Citations: 0] -
A new method to solve the forward and inverse problems for the spatial Solow model by using Physics Informed Neural Networks (PINNs)
Hu, Wanjuan
Engineering Analysis with Boundary Elements, Vol. 169 (2024), Iss. P.106013
https://doi.org/10.1016/j.enganabound.2024.106013 [Citations: 0] -
A Neural Network Method for Inversion of Turbulence Strength
Yin, Weishi | Zhang, Baoyin | Meng, Pinchao | Zhou, Linhua | Qi, DequanJournal of Nonlinear Mathematical Physics, Vol. 31 (2024), Iss. 1
https://doi.org/10.1007/s44198-024-00186-0 [Citations: 5] -
On the identification of small anomaly in microwave imaging without homogeneous background information
Park, Won-Kwang
AIMS Mathematics, Vol. 8 (2023), Iss. 11 P.27210
https://doi.org/10.3934/math.20231392 [Citations: 4]