ArticleMicromachines2023
Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis.
Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
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- Ensemble learning for air quality index prediction: integrating gradient boosting, XGBoost, and stacking with SHAP-based interpretability.Scientific reports · 2026Article
- KidneyTox_v1.0 enables explainable artificial intelligence prediction of nephrotoxicity in small molecules.Scientific reports · 2026Article
- AI/ML-Driven DPP-4 Inhibitor Predictor (d4p_v1) for Enhanced Type 2 Diabetes Mellitus Management: Insights Into Chemical Space, Fingerprints, and Electrostatic Potential Maps.Archiv der Pharmazie · 2025Article
- PPARγ modulator predictor (PGMP_v1): chemical space exploration and computational insights for enhanced type 2 diabetes mellitus management.Molecular diversity · 2025Article
- Interpretable Prediction and Analysis of PVA Hydrogel Mechanical Behavior Using Machine Learning.Gels (Basel, Switzerland) · 2025Article
- Predicting Long-Term Prognosis of Poststroke Dysphagia with Machine Learning.Journal of clinical medicine · 2025Article
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- Machine Learning Diagnostic Model for Early Stage NSTEMI: Using hs-cTnI 1/2h Changes and Multiple Cardiovascular Biomarkers.Diagnostics (Basel, Switzerland) · 2024Article
- Computational methods in glaucoma research: Current status and future outlook.Molecular aspects of medicine · 2023Review
- Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework.Diagnostics (Basel, Switzerland) · 2023Article
- CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures.Micromachines · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedance are closely related to the PCB production process, circuit designers and manufacturers must work together to adjust the target impedance to maintain signal integrity. Five machine learning models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM), were used to forecast target impedance values. Furthermore, the Optuna algorithm is used to determine forecasting model hyperparameters. This study applied tree-based machine learning techniques with Optuna to predict impedance. The results revealed that five tree-based machine learning models with Optuna can generate satisfying forecasting accuracy in terms of three measurements, including mean absolute percentage error (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.