Inversion for Equivalent Electromagnetic Parameters of Nonuniform Honeycomb Structures Based on BP Neural Network

• Published in: IEEE antennas and wireless propagation letters Vol. 23; no. 11; pp. 3982 - 3986
• Main Authors: He, Wei-Jia, Zhang, Yu-Xin, Wu, Bi-Yi, Sun, Sheng, Yang, Ming-Lin, Sheng, Xin-Qing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Back propagation networks; Backpropagation (BP) neural network; Cellular structure; Coatings; effective electromagnetic parameters; Electromagnetic scattering; Electromagnetics; Equivalence; Honeycomb structures; microwave absorbing honeycomb structure; Microwave absorption; Microwave theory and techniques; Multipoles; Neural networks; Parameters; Training
• ISSN: 1536-1225; 1548-5757
• DOI: 10.1109/LAWP.2024.3457785

In this letter, we introduce a backpropagation (BP) neural network-based inversion method for deriving the equivalent electromagnetic parameters of cellular microwave absorbing honeycomb structures. The conventional honeycomb structure is first homogenized into homogenous layers using the Hashin-Shtrikman (H-S) variational theory. Then, the sample honeycombs are generated by sampling the H-S unknown variables using prior knowledge of the physical and geometric characteristics of the honeycomb, and the training dataset are generated by computing the scattered field using the finite element-boundary integral-multilevel fast multipole algorithm. A BP neural network is trained using the scattered field from the sample honeycomb structures as the input, while the output is the undetermined variables for describing the equivalent electromagnetic parameters of the layered homogenous sample honeycomb using H-S theory. Numerical examples are presented to demonstrate the accuracy and effectiveness of the proposed BP neural network for predicting equivalent electromagnetic parameter of microwave absorbing honeycomb structures.