Knowledge-based artificial neural network models for finline

Finline plays an important role in millimeter-wave integrated-circuit design. In this paper, a knowledge-based artificial neural network is used to model the finline. Using prior knowledge input method and Bayesian regularization technique make the neural network models for finline reduce the amount of training data needed and prevent overfitting in neural network training. The neural network is electromagnetically developed with a set of training data that are produced by the finite element method, which is robust both from the angle of time of computation and accuracy.