Optimisation of reflection coefficient of microstrip antennas based on KBNN exploiting GPR model. Issue 4 (25th January 2018)

• Record Type: Journal Article
• Authors: Chen, Yi; Tian, Yu‐Bo; Qiang, Zhe; Xu, Lan
• Is Part Of: IET microwaves, antennas & propagation. Volume 12:Issue 4(2018)
• Page Start: 602
• Page End: 606
• Publication Date: 2018-01-25
• Subjects: neural nets -- regression analysis -- Gaussian processes -- microstrip antennas -- electromagnetic wave reflection -- telecommunication computing; reflection coefficient optimization -- microstrip antennas -- KBNN -- GPR model -- full‐wave electromagnetic simulation -- knowledge‐based neural network -- electromagnetic simulation -- Gaussian process regression -- prior knowledge input method; Microwaves -- Periodicals; Microwave antennas -- Periodicals; Antennas (Electronics) -- Periodicals; Radio wave propagation -- Periodicals; Microwave communication systems -- Periodicals; 621.381305
• DOI: 10.1049/iet-map.2017.0282
• Languages: English
• ISSNs: 1751-8725

Abstract

Abstract : 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.