Application of Two-Dimensional AWE Algorithm in Training Multi-Dimensional Neural Network Model - Xiong, Y.; Fang, D. G.; Chen, R. S. (Applied Computational Electromagnetics Society Journal (ACES), 2022)
Artificial neural network (ANN) plays very important role in microwave engineering. Training a neural network model is the key of neural network technique. The conventional methods for training, such as method of moment (MoM), are time-consuming when the training parameters are a bit more. In order to aid the training process by reducing the amount of costly and time-consuming sampling cycles, a lot of algorithms have been developed, such as asymptotic waveform evaluation (AWE). In this paper, MoM in conjunction with the two-dimensional AWE is applied to accelerate the process of training the neural network model based on the input impedance response on frequency and that on other parameters of a microstrip antenna. In AWE method, the derivatives of Green’s function are required. A closed form of microstrip Green’s function is used for this requirement. Then, the derivative matrices respect to both frequency and permittivity can be obtained from the original matrix. With these matrices in hand, coefficients of the two-dimensional Padé polynomial can be obtained. So the sampling data for training neural network model can be obtained and the process of training neural net model can be completed quickly and accurately. Numerical results demonstrate the efficiency of this technique.