KBNN Based on Coarse Mesh to Optimize the EBG Structures

Yi Chen,Fei-Yan Sun,Yu-Bo Tian

doi:10.1155/2017/3143846

Abstract

The microwave devices are usually optimized by combining the precise model with global optimization algorithm. However, this method is time-consuming. In order to optimize the microwave devices rapidly, the knowledge-based neural network (KBNN) is used in this paper. Usually, the a priori knowledge of KBNN is obtained by the empirical formulas. Unfortunately, it is difficult to derive the corresponding formulas for the most electromagnetic problems, especially for complex electromagnetic problems; the formula derivation is almost impossible. We use precise mesh model of EM analysis as teaching signal and coarse mesh model as a priori knowledge to train the neural network (NN) by particle swarm optimization (PSO). The NN constructed by this method is simpler than traditional NN in structure which can replace precise model in optimization and reduce the computing time. The results of electromagnetic band-gap (EBG) structures optimally designed by this kind of KBNN achieve increase in the bandwidth and attenuation of the stopband and small passband ripple level which shows the advantages of the proposed KBNN method.

Highlights

The electromagnetic band-gap (EBG) [1, 2] is a kind of artificial periodic structure that prohibits the propagation of electromagnetic waves in certain frequency bands at microwave frequencies
The a priori knowledge of knowledge-based neural network (KBNN) is always the empirical formula which contains the basic information about the microwave circuits but cannot achieve the required precision
If empirical formula is considered as a priori knowledge, the cost of calculation can be negligible, but not all microwave devices have equivalent circuit; if the a priori knowledge is obtained by neural network (NN), the training of NN requires a large number of samples: both of them are flawed

Summary

Introduction

The electromagnetic band-gap (EBG) [1, 2] is a kind of artificial periodic structure that prohibits the propagation of electromagnetic waves in certain frequency bands at microwave frequencies. If empirical formula is considered as a priori knowledge, the cost of calculation can be negligible, but not all microwave devices have equivalent circuit; if the a priori knowledge is obtained by NN, the training of NN requires a large number of samples: both of them are flawed. The a priori knowledge is obtained by coarse mesh model and is used as knowledge neurons in the hidden layers of NN. The advantage of this approach is that it can be widely applied to quick electromagnetic optimization even for the complex microwave devices. The method to build the KBNN model is given, including the neural network structure, acquisition of samples, and training method.

The Proposed KBNN

Simulation Examples of EBG Structures

Result of HFSS simulation after optimization

Conclusion