对称单面鳍线的人工神经网络模型

Artificial Neural Network Models for Symmetrical Unilateral Fin-Lines

采用知识人工神经网络模型拟对称平面鳍线,通过利用先验知识减小输入映射关系的复杂度,建立了知识人工神经网络模型,减少了训练样本的数量。同时保留了全波时域有限差分法的准确性,而且具有快速简便的优点。

In this paper, a knowledge-based artificial neural network is used to model the symmetrical unilateral fin-lines. Utilizing prior knowledge for reducing complexity of input-output relationships that the Artificial Neural Network (ANN) must learn, it allows an accurate ANN model to be developed with less training data which is very advantageous when training data is expensive/time Consuming to obtain, such as with EM simulation. The neural network is electromagnetically developed with a set of training data that are produced by the finite elemont method (FDTD). which is robust both from the angle of time of computation and accuracy.