Feature Selection for Determining Input Parameters in Antenna Modeling

In this paper, a feature selection method for determining input parameters in antenna modeling is proposed. In antenna modeling, the input feature of artificial neural network (ANN) is geometric parameters. The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic (EM) response. Maximal information coefficient (MIC), an exploratory data mining tool, is introduced to evaluate both linear and nonlinear correlations. The EM response range is utilized to evaluate the sensitivity. The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive. Only the parameter which is highly correlative and sensitive is selected as the input of ANN, and the sampling space of the model is highly reduced. The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method. The number of input parameters decreases from 8 to 4. The testing errors of |S 11 | and axis ratio are reduced by 8.74% and 8.95%, respectively, compared with the ANN with no feature selection.