Machine Learning Techniques for Optimizing Design of Double T-Shaped Monopole Antenna

Yashika Sharma, Hao Helen Zhang, Hao Xin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this communication, we propose using modern machine learning (ML) techniques including least absolute shrinkage and selection operator (lasso), artificial neural networks (ANNs), and k -nearest neighbor (kNN) methods for antenna design optimization. The automated techniques are shown to provide an efficient, flexible, and reliable framework to identify optimal design parameters for a reference dual-band double T-shaped monopole antenna to achieve favorite performance in terms of its two bands, i.e., between 2.4 and 3.0 and 5.15 and 5.6 GHz. In this communication, we also present a thorough study and comparative analysis of the results predicted by these ML techniques, with the results obtained from high-frequency structure simulator (HFSS) to verify the accuracy of these techniques.

Original languageEnglish (US)
Article number8962311
Pages (from-to)5658-5663
Number of pages6
JournalIEEE Transactions on Antennas and Propagation
Volume68
Issue number7
DOIs
StatePublished - Jul 2020

Keywords

  • Antenna optimization
  • least absolute shrinkage and selection operator (lasso) shrinkage
  • linear regression
  • machine learning (ML)
  • optimization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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