Patch Antenna Design Using Machine Learning: ANN and SVR

Aditi R. Patil; Chinmay M. Raju; Shreya Harish; P. Shanthi

doi:10.1109/CSITSS60515.2023.10334201

Patch Antenna Design Using Machine Learning: ANN and SVR

Aditi R. Patil
, Chinmay M. Raju
, Shreya Harish
, P. Shanthi

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Every advancement in technology aims to address challenges within a specific field. One such recognized issue pertains to the laborious process of antenna design utilizing CAD tools. The proposed solution offers an effective means to predict antenna output parameters, highlighting the converse-inefficiencies arising from suboptimal design and optimization procedures. Additionally, proficiency in using CAD tools becomes a prerequisite, demanding both skill acquisition and fluency. Thus, an imperative arises for an improved and streamlined antenna design process. Different methodologies exist for ascertaining antenna radiation parameters. In the provided approach, Machine Learning models playa pivotal role in establishing connections between radiation and input design parameters. Validation is achieved by comparing the outcomes with HFSS software simulation results.

Original language	English
Title of host publication	7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350343144
DOIs	https://doi.org/10.1109/CSITSS60515.2023.10334201
Publication status	Published - 2023
Event	7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Bangalore, India Duration: 02-11-2023 → 04-11-2023

Publication series

Name	7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings

Conference

Conference	7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023
Country/Territory	India
City	Bangalore
Period	02-11-23 → 04-11-23

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Information Systems
Software
Information Systems and Management
Artificial Intelligence
Renewable Energy, Sustainability and the Environment
Safety, Risk, Reliability and Quality

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CSITSS60515.2023.10334201

Cite this

Patil, A. R., Raju, C. M., Harish, S., & Shanthi, P. (2023). Patch Antenna Design Using Machine Learning: ANN and SVR. In 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings (7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSITSS60515.2023.10334201

Patil, Aditi R. ; Raju, Chinmay M. ; Harish, Shreya et al. / Patch Antenna Design Using Machine Learning : ANN and SVR. 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings).

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title = "Patch Antenna Design Using Machine Learning: ANN and SVR",

abstract = "Every advancement in technology aims to address challenges within a specific field. One such recognized issue pertains to the laborious process of antenna design utilizing CAD tools. The proposed solution offers an effective means to predict antenna output parameters, highlighting the converse-inefficiencies arising from suboptimal design and optimization procedures. Additionally, proficiency in using CAD tools becomes a prerequisite, demanding both skill acquisition and fluency. Thus, an imperative arises for an improved and streamlined antenna design process. Different methodologies exist for ascertaining antenna radiation parameters. In the provided approach, Machine Learning models playa pivotal role in establishing connections between radiation and input design parameters. Validation is achieved by comparing the outcomes with HFSS software simulation results.",

author = "Patil, \{Aditi R.\} and Raju, \{Chinmay M.\} and Shreya Harish and P. Shanthi",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 ; Conference date: 02-11-2023 Through 04-11-2023",

year = "2023",

doi = "10.1109/CSITSS60515.2023.10334201",

language = "English",

series = "7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings",

address = "United States",

}

Patil, AR, Raju, CM, Harish, S & Shanthi, P 2023, Patch Antenna Design Using Machine Learning: ANN and SVR. in 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings. 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023, Bangalore, India, 02-11-23. https://doi.org/10.1109/CSITSS60515.2023.10334201

Patch Antenna Design Using Machine Learning: ANN and SVR. / Patil, Aditi R.; Raju, Chinmay M.; Harish, Shreya et al.
7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Patch Antenna Design Using Machine Learning

T2 - 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

AU - Patil, Aditi R.

AU - Raju, Chinmay M.

AU - Harish, Shreya

AU - Shanthi, P.

PY - 2023

Y1 - 2023

N2 - Every advancement in technology aims to address challenges within a specific field. One such recognized issue pertains to the laborious process of antenna design utilizing CAD tools. The proposed solution offers an effective means to predict antenna output parameters, highlighting the converse-inefficiencies arising from suboptimal design and optimization procedures. Additionally, proficiency in using CAD tools becomes a prerequisite, demanding both skill acquisition and fluency. Thus, an imperative arises for an improved and streamlined antenna design process. Different methodologies exist for ascertaining antenna radiation parameters. In the provided approach, Machine Learning models playa pivotal role in establishing connections between radiation and input design parameters. Validation is achieved by comparing the outcomes with HFSS software simulation results.

AB - Every advancement in technology aims to address challenges within a specific field. One such recognized issue pertains to the laborious process of antenna design utilizing CAD tools. The proposed solution offers an effective means to predict antenna output parameters, highlighting the converse-inefficiencies arising from suboptimal design and optimization procedures. Additionally, proficiency in using CAD tools becomes a prerequisite, demanding both skill acquisition and fluency. Thus, an imperative arises for an improved and streamlined antenna design process. Different methodologies exist for ascertaining antenna radiation parameters. In the provided approach, Machine Learning models playa pivotal role in establishing connections between radiation and input design parameters. Validation is achieved by comparing the outcomes with HFSS software simulation results.

UR - https://www.scopus.com/pages/publications/85181538658

UR - https://www.scopus.com/pages/publications/85181538658#tab=citedBy

U2 - 10.1109/CSITSS60515.2023.10334201

DO - 10.1109/CSITSS60515.2023.10334201

M3 - Conference contribution

AN - SCOPUS:85181538658

T3 - 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings

BT - 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 2 November 2023 through 4 November 2023

ER -

Patil AR, Raju CM, Harish S, Shanthi P. Patch Antenna Design Using Machine Learning: ANN and SVR. In 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings). doi: 10.1109/CSITSS60515.2023.10334201

Patch Antenna Design Using Machine Learning: ANN and SVR

Abstract

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All Science Journal Classification (ASJC) codes

UN SDGs

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