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International Journal of Antennas and Propagation
Volume 2017, Article ID 3143846, 9 pages
Research Article

KBNN Based on Coarse Mesh to Optimize the EBG Structures

1School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China
2Nanjing Software Institute, Jinling Institute of Technology, Nanjing, Jiangsu 211169, China

Correspondence should be addressed to Yu-bo Tian; nc.ude.tsuj@obuynait

Received 11 July 2016; Revised 22 December 2016; Accepted 11 January 2017; Published 2 February 2017

Academic Editor: Shih Yuan Chen

Copyright © 2017 Yi Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.