Automatic Coupler Design Based on Artificial Neural Network With Self-Adaptive Local Surrogates

A new artificial neural network (ANN) inverse modeling method with self-adaptive local surrogates, called ANN-SALS, is presented in this article, for a large-scale beam-forming network (BFN). To get accurate dimensions of the huge number of directional couplers, constituting a large BFN could be com...

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Bibliographic Details
Published in:IEEE transactions on microwave theory and techniques 2022-11, Vol.70 (11), p.4711-4725
Main Authors: Liu, Anlan, Leng, Maoheng, Pan, Guangyuan, Yu, Ming
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural network (ANN)
Artificial neural networks
Beamforming
Computational modeling
Design specifications
directional coupler
Directional couplers
Gaussian process
Gaussian process regression (GPR)
Inverse problems
Local optimization
Modelling
multispecification design problem
Neural networks
Prediction algorithms
Scattering parameters
Sensitivity analysis
Specifications
surrogate-assisted design
Training
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Summary:A new artificial neural network (ANN) inverse modeling method with self-adaptive local surrogates, called ANN-SALS, is presented in this article, for a large-scale beam-forming network (BFN). To get accurate dimensions of the huge number of directional couplers, constituting a large BFN could be computationally expensive. Here, we develop an automated and more efficient algorithm to design multiple couplers with different specifications simultaneously using shared data for local surrogate modeling. While the result predicted by ANN serves as the starting point, a Gaussian process (GP) local surrogate model is built around it for fine-tuning. Then, ANN database is updated by selected GP training samples as well as the fine-tuned solution. After carefully setting the design specification, GP training data can be shared among the design of multiple couplers despite different specifications so that the design of one coupler is improved by the other ones. What is more, the size of region where GP is built is corrected by a quasi-sensitivity analysis method to improve the local optimization success rate. To verify the algorithm, four different couplers with different design requirements are designed, showing the proposed technique could unravel various multiple coupler design problems automatically and is much more efficient compared with existing methods.
ISSN:0018-9480
1557-9670
DOI:10.1109/TMTT.2022.3184024