Neural network implementation for dumb bell antenna
File(s)
Author(s)
Kumar, Varindra
Type
Conference Paper
Abstract
This paper presents an implementation of compact and small Dumb bell dipole antenna for its resonance at 3.5 GHz. An array has been created to provide the highest gain at its resonance frequency and has been compared for its parametric variation. The reflection parameter and gain for various configurations such as x1 element, x2 and x2x2 array has been calculated and compared with. The radiation pattern for these configurations has also been calculated and shown. An efficient and fast feed forward neural network has been designed and trained using Matlab to calculate the gain of the array antenna using the gain of the single element antenna within a small margin of error. Due to its integration within impedance controlled PCB environment, an impedance matching circuit using a simple tuning circuit has also been obtained and shown here.
Date Acceptance
2020-12-24
Citation
Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, 237, pp.509-522
ISSN
2367-3389
Publisher
Springer
Start Page
509
End Page
522
Journal / Book Title
Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications
Volume
237
Copyright Statement
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. The final publication is available at Springer via https://doi.org/10.1007/978-981-16-6407-6_45
Source
International conference on Recent Trends in Machine Learning, IOT, Smart Cities & Applications (ICMISC 2021)
Publication Status
Published
Start Date
2021-03-27
Finish Date
2020-03-29
Coverage Spatial
Hyderabad, India
Date Publish Online
2022-01-01