Publication: Machine learning modeling for s-parameters prediction of 5x5 via-array on 10 cavity pcb
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Date
2023-07
Authors
Lim, Jun Liang
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Abstract
This project aims to address the challenges and limitations of machine learning based ML modeling, specifically when applied to large databases. The focus of the project is to predict the S-parameters of a 5x5 Via-Array on a 10 Cavity PCB using various machine learning (ML) models. The primary objective is to determine the most accurate ML model for a sizable database. The investigated ML models include Support Vector Machines (SVM), Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), regression trees, ensembles of trees, and linear regression models. The research will also explore techniques for mitigating overfitting in machine learning models and approaches for reducing training time through sampling techniques. By evaluating the performance of these ML models on the given database, this project seeks to provide insights into the effectiveness of different approaches for predicting the S-parameters. Furthermore, it aims to offer recommendations for avoiding overfitting and optimizing training time. The findings of this research will contribute to the advancement of machine learning-based AI modeling in handling large datasets and improve the accuracy of predictions in the context of the 5x5 Via-Array on a 10 Cavity PCB.