基于机器的自动零碎极端学习模型,用于RF功率放大器的S-参数
Lulu Wang1,2, Shaohua Zhou3,4,5, Wenrao Fang2
1School of Micro-Nano Electronics, Zhejiang University, Hangzhou 310058, China.
Micromachines
|July 8, 2023
概括
本研究介绍了一种用于模拟射频功率放大器S参数的自动零碎极端学习机器 (ELM). 与现有技术相比,新方法显著提高了建模速度和准确性.
科学领域:
- 电气工程 电气工程
- 计算智能是一种计算智能.
背景情况:
- 精确的无线电频率 (RF) 功率放大器 (PA) 建模对于现代通信系统至关重要.
- 现有的建模技术,如长期短期记忆 (LSTM),支持向量回归 (SVR) 和传统的极端学习机器 (ELM) 在速度和准确性方面面临挑战.
研究的目的:
- 开发一种高效准确的自动零碎 (Auto-PW) 极端学习机器 (ELM) 方法,用于RF PA的S参数建模.
- 为了提高RF PA S参数的建模速度和准确性.
主要方法:
- 提出了一种自动零碎 (Auto-PW) 极端学习机器 (ELM) 方法.
- 实施了基于凸凸的特征变化点的区分战略.
- 针对不同地区逐个开发 ELM 模型.
主要成果:
- 自动PW ELM方法在从2.2-6.5GHz CMOS PA.测量到的S参数上表现出了卓越的性能.
- 实现的建模速度比SVR和LSTM快两倍.
- 获得的建模准确度比传统的ELM高出一个数量级以上.
结论:
- 自动PW ELM方法在RF PA S参数建模中提供了显著的进步.
- 这种方法为现有的建模技术提供了更快,更准确的替代方案.
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