PSO‐GA‐SVR model for S‐parameters of radio‐frequency power amplifier under different temperatures

Abstract

AbstractSupport 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.