Gwo-ga-xgboost-based model for Radio-Frequency power amplifier under different temperatures

Abstract

To improve the modeling accuracy and modeling speed of the XGBoost model, a Gray Wolf Optimization (GWO)-Genetic Algorithm (GA)-XGBoost model is proposed in this paper and is applied to model radio frequency (RF) power amplifiers at different temperatures. The experimental results show that compared to XGBoost, GA-XGBoost, and GWO-XGBoost, the modeling accuracy of GWO-GA-XGBoost can be improved by one order of magnitude or more. Compared to XGBoost, GA-XGBoost, and GWO-XGBoost, GWO-GA-XGBoost has also increased the modeling speed by one magnitude or more. In addition, compared to the classic machine learning algorithms gradient boosting, random forest, and AdaBoost, the proposed GWO-GA-XGBoost can improve modeling accuracy by two or more orders of magnitude while also increasing modeling speed by one or more.