Antenna optimization based on master-apprentice broad learning system

In order to improve the efficiency of antenna optimization design, a surrogate model is often used to replace the full-wave electromagnetic simulation software. Broad learning system (BLS) provides an alternative method for deep structure, aiming to overcome the drawback of excessive time-consuming...

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Bibliographic Details
Published inInternational journal of machine learning and cybernetics Vol. 13; no. 2; pp. 461 - 470
Main Authors Ding, Weitong, Tian, Yubo, Li, Pengfei, Yuan, Huining, Li, Rui
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2022
Springer Nature B.V
Subjects
Algorithms
Antenna design
Approximation
Artificial Intelligence
Complex Systems
Computational Intelligence
Computer simulation
Control
Design optimization
Engineering
Learning
Mechatronics
Microstrip antennas
Monopole antennas
Neural networks
Optimization
Original Article
Pattern Recognition
Robotics
Software
Subsystems
Systems Biology
Training
Broad learning system
Master-apprentice behavior
Full-wave electromagnetic simulation software
Antenna optimization
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Summary:In order to improve the efficiency of antenna optimization design, a surrogate model is often used to replace the full-wave electromagnetic simulation software. Broad learning system (BLS) provides an alternative method for deep structure, aiming to overcome the drawback of excessive time-consuming training process, however, usually not with satisfactory accuracy. In order to further improve the performance of the model, master-apprentice (MA) behavior is proposed in this paper, using the current BLS training results as the priori knowledge, which are taken as fixed features to the next BLS hidden layer for further training. Each MA behavior forms a double BLS structure, which is composed of two parts, the models trained before and after are called master BLS (MBLS) and apprentice BLS (ABLS) respectively. These two subsystems together constitute a master-apprentice BLS (MABLS). Two antenna examples, rectangular microstrip antenna (RMSA) and WLAN dual-band monopole antenna (DBMA), and 10 UCI regression datasets are employed to demonstrate the effectiveness of the proposed model.
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-021-01418-1