%0 Electronic Article
%A Yi Chen
%A Yu-Bo Tian
%A Zhe Qiang
%A Lan Xu
%K GPR model
%K KBNN
%K prior knowledge input method
%K Gaussian process regression
%K electromagnetic simulation
%K full-wave electromagnetic simulation
%K microstrip antennas
%K reflection coefficient optimization
%K knowledge-based neural network
%X When microstrip antennas (MSAs) are optimised, the full-wave electromagnetic simulation takes long time to get the result which is very time consuming. Hence, the knowledge-based neural network (KBNN) is used here to replace the electromagnetic simulation to shorten the calculating time and raise efficiency. However, prior knowledge of KBNN is always obtained by empirical formulas and neural networks, both of them are heavy and complicated. In this study, Gaussian process regression (GPR) is proposed to get the prior knowledge. The modelling of MSA is realised by the prior knowledge input method. The short optimal time and excellent optimal results prove that the proposed KBNN is a fast and effective method for the optimisation of MSAs.
%@ 1751-8725
%T Optimisation of reflection coefficient of microstrip antennas based on KBNN exploiting GPR model
%B IET Microwaves, Antennas & Propagation
%D March 2018
%V 12
%N 4
%P 602-606
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=122iztzjexawq.x-iet-live-01content/journals/10.1049/iet-map.2017.0282
%G EN