Improving Hardenability Modeling: A Bayesian Optimization Approach to Tuning Hyperparameters for Neural Network Regression

Improving Hardenability Modeling: A Bayesian Optimization Approach to Tuning Hyperparameters for Neural Network Regression

This study investigates the application of regression neural networks, particularly the <i>fitrnet</i> model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schem...

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Bibliographic Details
Main Authors: Wendimu Fanta Gemechu, Wojciech Sitek, Gilmar Ferreira Batalha
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Applied Sciences
Subjects:
steel hardenability
Bayesian optimization
k-fold cross-validation
hyperparameter tuning
neural network regression
steel alloy
Online Access:https://www.mdpi.com/2076-3417/14/6/2554

Internet

https://www.mdpi.com/2076-3417/14/6/2554

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