基于PSO-ELM的GaN PA的温度特征建模
Qian Lin1,2,3, Meiqian Wang1
1School of Intelligent Science and Engineering, Qinghai Minzu University, Xining 810007, China.
Micromachines
|August 29, 2024
概括
粒子集群优化-极端学习机器 (PSO-ELM) 模型化高电子移动性晶体管 (GaN HEMT) 功率放大器性能. 比起ELM,PSO-ELM显示出更高的预测准确度,有助于优化功率放大器设计.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 计算智能是一种计算智能.
背景情况:
- 功率放大器 (PA) 是电子系统中的关键组件.
- 准确的性能预测和PA的设计优化是必不可少的.
- 高电子移动性化物晶体管 (GaN HEMT) 提供高效率和功率.
研究的目的:
- 在不同温度下建模 GaN HEMT PAs 的性能参数.
- 为了比较粒子优化-极端学习机器 (PSO-ELM) 和极端学习机器 (ELM) 的预测能力.
- 为优化PA设计提供理论支持.
主要方法:
- 使用 PSO-ELM 和 ELM 算法建模 GaN HEMT PA 的性能.
- 基于平均平方误差 (MSE) 评估模型准确性.
- 分析模型在处理温度依赖的非线性时的概括能力.
主要成果:
- 与ELM模型相比,PSO-ELM模型显示出更高的预测准确性.
- 用PSO-ELM模型实现了0.0006的最低MSE.
- 对于与温度相关的PA性能,PSO-ELM表现出更强的概括能力.
结论:
- PSO-ELM是一种有效的方法,用于模拟不同温度的GaN HEMT PA性能.
- 提高PSO-ELM的概括能力,支持准确的性能预测.
- 这项研究为优化PA设计和性能提供了宝贵的理论见解.
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