Optimal Design of Conical Horn Antenna Based on GP Model with Coarse Mesh

  • Yong-xiu XuEmail author
  • Yu-bo Tian
Research Paper


Gauss process (GP) is a learning machine which has developed rapidly in recent years. Compared with the methods of artificial neural network and support vector machine, GP is easy to implement and has the advantages of adaptive acquisition and predictive output. This paper presents a modeling method based on GP. When constructing the proposed GP model, the input samples of the model calculates the results of electromagnetic simulation software with coarse mesh, and the corresponding output samples are these of electromagnetic simulation software with precise mesh. Based on the proposed GP model exploiting particle swarm optimization (PSO) algorithm, a dual-mode conical horn antenna is optimized, and the optimization results are perfect. The modeling process and computing results show that the proposed GP model based on the coarse mesh can greatly reduce the mapping relation between the input and output, and its generalization ability is excellent. Moreover, in the optimization process, the trained GP model can be used to evaluate the fitness function of PSO, and the time required for optimization is obviously reduced because it can give the fitness function value rapidly.


Gauss process Particle swarm optimization Conical horn antenna 



This work is supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province China under No. KYCX17_1840, the National Natural Science Foundation of China (NSFC) under No. 61771225 and the Key Research and Development Program Project of Social Development in Jiangsu Province under No. BE2016723.


  1. Chen T, Liu L (2007) Design of dual-band conical horn antenna. J Radio Wave Sci 22(6):991–994MathSciNetGoogle Scholar
  2. Chen F, Tian Y (2014) Modeling resonant frequency of rectangular microstrip antenna using CUDA-based artificial neural network trained by particle swarm optimization algorithm. Appl Comput Electromagn Soc J 29(12):1025–1034Google Scholar
  3. Chen Y, Tian Y, Sun F (2017) KBNN based on coarse mesh to optimize the ebg structures. Int J Antennas Propag 1(4):155–164Google Scholar
  4. Ding XL, Xu L (2014) Design of double mode conical horn antenna. J Telem 6(3):13–15Google Scholar
  5. Hao Z, Ge H, Gu T (2017) Automatic image annotation based on particle swarm optimization and support vector clustering. Int J Antennas Propag 5(18):47–58Google Scholar
  6. Jacobs J, Koziel S (2014) Two-stage framework for efficient Gaussian process modeling of antenna input characteristics. IEEE Trans Antennas Propag 62(2):706–713CrossRefGoogle Scholar
  7. Liu B, Zhang QF (2014) A Gaussian process surrogate model assisted evolutionary algorithm for medium scale expensive optimization problems. IEEE Trans Evol Comput 18(2):180–192CrossRefGoogle Scholar
  8. Malathi P, Kumar R (2009) On the design of multilayer circular microstrip antenna using artificial neural networks. Int J Recent Trends Eng 2(1):70–74Google Scholar
  9. Mallahzadeh AR, Dastranj AA, Akhlaghi S (2008) Quad-ridged conical horn antenna for wideband applications. Fac Eng 12(29):519–528Google Scholar
  10. Mkadem F, Boumaiza S (2000) Extended Hammerstein behavioral model using artificial neural networks. IEEE Trans Microw Theory Tech 57(4):745–751CrossRefGoogle Scholar
  11. Pieter J, Koziel S, Ogurtsov S (2012) Low-cost variable fidelity Bayesian support vector machine modeling of planar slot antennas. In: IEEE conferences, antennas and propagation (EUCAP), vol 43, no 4, pp 3086–3088Google Scholar
  12. Qiang Z, Chen Y, Tian Y (2016) Optimization of GPS antenna by PSO-based GP modeling. Chin J Radio Sci 31(5):927–932Google Scholar
  13. Sun F, Tian Y, Ren Z (2016) Modeling the resonant frequency of compact microstrip antenna by the PSO-based SVM with the hybrid kernel function. Int J Numer Model Electron Netw Devices Fields 29(6):1129–1139CrossRefGoogle Scholar
  14. Thakare VV, Singhal P (2010) Microstrop anternna design using artificial neural networks. Int J RF Microwave Comput Aided Eng 20(1):76–86Google Scholar
  15. Tian Y (2014) Particle swarm optimization algorithm and electromagnetic applications. Science Press, BeijingGoogle Scholar
  16. Tian Y, Zhang S, Li J (2012) Modeling resonant frequency of microstrip antenna based on neural network ensemble. Int J Numer Model Electron Netw Devices Fields 24(1):78–88zbMATHGoogle Scholar
  17. Tokan NT, Gunes F (2005) Support vector characterization of the microstrip antennas based on measurements. Prog Electromagn Res 5(8):49–61Google Scholar
  18. Villiers JP, Jacobs JP (2009) Gaussian process modeling of CPW-fed slot antennas. Prog Electromagn Res 98(21):233–249CrossRefGoogle Scholar
  19. Wang BZ, Zhao D, Hong J (2010) Modeling stripline discontinuities by neural network with knowledge-based neurons. IEEE Trans Adv Packag 23(4):692–698Google Scholar
  20. Wang Z, Fang S, Wang Q (2012) An ANN-based synthesis model for the single-feed circularly-polarized square microstrip antenna with truncated corner. IEEE Trans Antennas Propag 60(12):5989–5992CrossRefGoogle Scholar
  21. Wong KL (2004) Compact and broadband microstrip antennas. Wiley, New YorkGoogle Scholar
  22. Zhou X, Jiang P, Wang X (2015a) Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function. J Intell Manuf 29(4):1–17Google Scholar
  23. Zhou L, Chen J, Song Z (2015b) Recursive Gaussian process regression model for adaptive quality monitoring in batch processes. Int J Antennas Propag 6(21):949–959Google Scholar

Copyright information

© Shiraz University 2019

Authors and Affiliations

  1. 1.School of Electronics and InformationJiangsu University of Science and TechnologyZhenjiangChina

Personalised recommendations