Neural networks (NNs) are efficient techniques for behavioral modeling of power amplifiers (PAs). This letter proposes a genetic algorithm to determine the optimal hyperparameters of the NN model for a PA. Different activation functions are compared. The necessary number of training epochs is also studied to get an optimal solution with a significantly reduced computational complexity. Experimental measurements on a PA with different signals validate the NN models determined by the proposed method.

中文翻译：

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两隐藏层神经网络的超参数优化功率放大器行为建模使用遗传算法

神经网络 (NN) 是功率放大器 (PA) 行为建模的有效技术。这封信提出了一种遗传算法来确定 PA 的 NN 模型的最佳超参数。比较了不同的激活函数。还研究了必要的训练时期数，以获得具有显着降低计算复杂度的最佳解决方案。对具有不同信号的 PA 的实验测量验证了由所提出的方法确定的 NN 模型。