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Autores principales: Balaji, Bharath, Raghavan, S.
Formato: Preprint
Publicado: 2024
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Acceso en línea:https://arxiv.org/abs/2406.04357
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author Balaji, Bharath
Raghavan, S.
author_facet Balaji, Bharath
Raghavan, S.
contents This study, conducted in 2017, explores the use of Machine learning algorithms to predict Characteristics of Transmission Lines such as Impedance or resonance frequency using design parameters of Transmission Lines. Using formulas and equations that define the characteristics of Transmission lines, training data was generated. We trained different models for this dataset. The extent of deviation of predicted output from the actual output was measured in terms of maximum error and average error. This helped determine how well an algorithm worked for a particular transmission line. Further, the best-suited algorithm for each transmission line under consideration was found based on the error
format Preprint
id arxiv_https___arxiv_org_abs_2406_04357
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Using Machine Learning to predict Characteristics of Microstrip Line and Microstrip Patch Antenna
Balaji, Bharath
Raghavan, S.
Signal Processing
This study, conducted in 2017, explores the use of Machine learning algorithms to predict Characteristics of Transmission Lines such as Impedance or resonance frequency using design parameters of Transmission Lines. Using formulas and equations that define the characteristics of Transmission lines, training data was generated. We trained different models for this dataset. The extent of deviation of predicted output from the actual output was measured in terms of maximum error and average error. This helped determine how well an algorithm worked for a particular transmission line. Further, the best-suited algorithm for each transmission line under consideration was found based on the error
title Using Machine Learning to predict Characteristics of Microstrip Line and Microstrip Patch Antenna
topic Signal Processing
url https://arxiv.org/abs/2406.04357