Semisupervised Radial Basis Function Neural Network with an Effective Sampling Strategy

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.

Duke Scholars

Author William T. Joines Electrical and Computer Engineering