This study performed modeling of the scattering (S) and noise (N) parameters of the ATF53189 using the General Regression Neural Network (GRNN) and Multi Layer Perceptron Neural Network (MLPNN) methods based on Artificial Neural Network (ANN). For modeling the linear behavior of the transistor, the optimum design parameters of the GRNN and the MLPNN methods were determined using four different optimization algorithms. These are whale optimization algorithm (WOA), artificial bee colony (ABC), particle swarm optimization (PSO) and ant lion optimizer (ALO) algorithms. With the help of these algorithms, the sigma parameter of the GRNN and the number of hidden layers, numbers of neurons in the hidden layers and the activation functions of the hidden layers of the MLPNN were optimized. This way, the best models required for prediction of the S and N parameters of the ATF53189 were obtained. Different models that provided each of the angles and magnitudes of the S₁₁, S₂₁, S₁₂, S₂₂ parameters and the F_min, Γ_min, Γ_opt magnitude, Γ_opt angle and R_n noise parameters as the output were created. The experimental results showed that the GRNN method should be used in linear behavior modeling of S parameters of the ATF53189 and the MLPNN method should be used in linear behavior modeling of N parameters of the ATF53189. It is understood that the best algorithm for optimizing the design parameters of the GRNN and the MLPNN methods is the PSO. As a result, the modeling of the S and N parameters of the ATF53189 transistor was successfully carried out with the methods used in this study.

中文翻译：

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微波晶体管散射和噪声参数建模的新型神经网络优化方法

本研究对散射 ( S ) 和噪声 ( N) 的 ATF53189 参数，使用基于人工神经网络 (ANN) 的通用回归神经网络 (GRNN) 和多层感知器神经网络 (MLPNN) 方法。为了模拟晶体管的线性行为，GRNN 和 MLPNN 方法的最佳设计参数是使用四种不同的优化算法确定的。这些是鲸鱼优化算法 (WOA)、人工蜂群 (ABC)、粒子群优化 (PSO) 和蚁狮优化器 (ALO) 算法。借助这些算法，优化了 GRNN 的 sigma 参数和隐藏层数、隐藏层神经元数和 MLPNN 隐藏层的激活函数。这样，预测S和N所需的最佳模型获得了 ATF53189 的参数。创建了提供S ₁₁、S ₂₁、S ₁₂、S ₂₂参数的每个角度和幅度以及F _min、Γ _min、Γ _opt幅度、Γ _opt角度和R _n噪声参数作为输出的不同模型。实验结果表明，ATF53189的S参数线性行为建模应采用GRNN方法，N的线性行为建模应采用MLPNN方法。ATF53189 的参数。据了解，优化 GRNN 和 MLPNN 方法设计参数的最佳算法是 PSO。因此，使用本研究中使用的方法成功地对 ATF53189 晶体管的S和N参数进行了建模。