To alleviate the nonuniform error distribution and slow convergence caused by the uncertainty of sample selection in the training process of artificial neural networks, a semisupervised radial basis function neural network (SS-RBFNN) model with a new sampling strategy is proposed for parametric modeling of microwave components in this article. After evaluating the current training performance, the new sampling strategy selects suitable training samples to ensure each subregion of the whole sampling region with the same level of training and testing accuracy. Meanwhile, the proposed SS-RBFNN simplifies the modeling process to further enhance the modeling accuracy and efficiency. Two numerical examples of a slow-wave defected ground structure dual-band bandpass filter and a microstrip-to-microstrip vertical transition are employed to verify the effectiveness of the proposed model.

中文翻译：

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具有有效采样策略的半监督径向基函数神经网络

为了缓解人工神经网络训练过程中样本选择不确定性导致的误差分布不均匀和收敛速度慢的问题，提出了一种采用新采样策略的半监督径向基函数神经网络（SS-RBFNN）模型用于微波参数建模。本文中的组件。在评估当前的训练性能后，新的采样策略选择合适的训练样本，以保证整个采样区域的每个子区域具有相同水平的训练和测试精度。同时，所提出的SS-RBFNN简化了建模过程，进一步提高了建模精度和效率。