WFM-LBS Weighted Fusion Model in Thermal Characteristic Prediction of High Power Density Printed Circuit Boards

Increased operating temperatures significantly compromise the reliability of high-density printed circuit boards (PCBs). While traditional thermal evaluation methods struggle with efficiency under complex conditions, machine learning offers a robust alternative through nonlinear modeling. This paper develops a weighted fusion model (WFM-LBS) for predicting the thermal performance of high-power-density PCBs. By integrating Linear Regression with Gradient Descent (LR-GD), Back Propagation Neural Network (BPNN), and Support Vector Regression (SVR) as base learners, the model utilizes the Entropy Weight Method (EWM) to optimize weight assignment. Comparative validation demonstrates that WFM-LBS effectively synergetizes the strengths of individual predictors, significantly enhancing prediction accuracy and stability. This approach provides a reliable foundation for real-time thermal monitoring and intelligent management of high-performance cooling systems.