在不同温度下使用射频功率放大器的 CS-GA-XGBoost 模型
Jiayi Wang1,2, Shaohua Zhou2,3,4
1School of Micro-Nano Electronics, Zhejiang University, Hangzhou 310058, China.
Micromachines
|September 28, 2023
概括
一个新的CS-GA-XGBoost模型显著提高了功率放大器 (PA) 建模的准确性和速度. 这种先进的机器学习方法优于传统方法,为射频/微波设备建模提供了显著的改进.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 方法,如支持向量回归 (SVR) 和梯度提升,用于功率放大器 (PA) 建模.
- XGBoost提供高精度建模,但需要最佳的超参数调整.
- 传统的超参数搜索 (例如网格搜索) 是低效且耗时的.
研究的目的:
- 开发一种高效准确的PA建模方法.
- 为了解决传统超参数优化的局限性.
- 为了提高PAs的建模精度和速度.
主要方法:
- 提出了一种使用混合Cuckoo Search (CS) -遗传算法 (GA) 优化XGBoost (CS-GA-XGBoost) 的新型PA建模方法.
- 将GA的交叉运营商集成到CS中,以利用全球搜索和快速融合.
- 通过使用2.5-GHz的GaN类EPA在各种温度 (-40°C,25°C,125°C) 上测量的数据验证了该方法.
主要成果:
- 与XGBoost,GA-XGBoost和CS-XGBoost相比,CS-GA-XGBoost的建模精度提高了超过一个数量级.
- 与XGBoost,GA-XGBoost和CS-XGBoost相比,CS-GA-XGBoost将建模时间缩短了一级以上.
- 性能优于梯度增强,随机森林和SVR,精度为三次数,速度为两次数.
结论:
- CS-GA-XGBoost 方法在 PA 建模准确性和速度方面表现出卓越的性能.
- 这种混合优化方法有效地解决了超参数调整的挑战.
- CS-GA-XGBoost模型显示了在无线电频率/微波设备和电路建模中应用的巨大潜力.
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