Support vector machine (SVR) has been introduced into the modeling of S-parameters in radio-frequency (RF) power amplifiers (PA). The modeling accuracy and speed of SVR are primarily affected by the penalty parameter c and the kernel function coefficient γ. Using the traditional grid search technique to determine these two parameters is time-consuming and labor-intensive, and ensuring the model's accuracy is not easy. This article proposes an S-parameters modeling method based on PSO-GA-SVR to improve the SVR's modeling accuracy and speed. The model mainly focuses on particle swarm optimization (PSO) and combines selection, crossover, and mutation operations in genetic algorithms (GA). The fitness values are arranged from small to large in each iteration process, and the first 1/3 are selected for crossover and mutation. Then, the resulting new particle swarms are introduced into the original particle swarm population for searching. On the one hand, PSO-GA extends the population size and reduces the possibility of falling into local optimization. On the other hand, due to population size expansion, the number of iteration rounds is reduced, and the modeling speed is also increased. The experimental results show that compared to SVR, GA-SVR, and PSO-SVR, the proposed PSO-GA-SVR can improve the modeling accuracy by more than one magnitude or more while also increasing modeling speed by one magnitude or more. Furthermore, compared with the classical machine learning algorithms such as gradient boosting, random forest, and gcForset, the proposed PSO-GA-SVR improves the modeling accuracy by one order of magnitude and the modeling speed by two orders of magnitude more than gradient boosting, random forest, and improves the modeling speed by one order of magnitude more than gcForset.

中文翻译：

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不同温度下射频功率放大器S参数的PSO-GA-SVR模型

支持向量机 (SVR) 已被引入射频 (RF) 功率放大器 (PA) 的 S 参数建模中。SVR的建模精度和速度主要受惩罚参数c和核函数系数γ的影响。使用传统的网格搜索技术来确定这两个参数既费时又费力，而且保证模型的准确性也不容易。本文提出一种基于PSO-GA-SVR的S参数建模方法，以提高SVR的建模精度和速度。该模型主要关注粒子群优化（PSO），并结合遗传算法（GA）中的选择、交叉和变异操作。每次迭代过程中适应度值从小到大排列，选择前1/3进行交叉和变异。然后，将生成的新粒子群引入到原始粒子群种群中进行搜索。一方面，PSO-GA扩大了种群规模，降低了陷入局部优化的可能性。另一方面，由于种群规模的扩大，迭代轮数减少，建模速度也提高。实验结果表明，与SVR、GA-SVR和PSO-SVR相比，所提出的PSO-GA-SVR可以将建模精度提高一个数量级以上，同时将建模速度提高一个数量级以上。此外，与梯度提升、随机森林和gcForset等经典机器学习算法相比，所提出的PSO-GA-SVR比梯度提升提高了一个数量级的建模精度和两个数量级的建模速度，随机森林，建模速度比 gcForset 提高一个数量级。