Skip to main content
SpringerLink
Log in

A comparative study on parameters of leaf-shaped patch antenna using hybrid artificial intelligence network models

  • New Trends in data pre-processing methods for signal and image classification
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This study proposes a very compact coaxial-fed planar antenna for X band applications. The antenna design includes a tulip-shaped radiator on the FR4 dielectric substrate. The antenna parameters, such as return losses, bandwidth and operating frequency, have close relationships with patch geometry. In order to obtain desired antenna parameters for X band application, patch dimension is necessary to be optimized. In this article, four different hybrid artificial intelligence network models are suggested for optimization. These are particle swarm optimization, differential evolution, grey wolf optimizer and vortex search algorithm. Also, they are combined with artificial neural network for the purpose of estimating dimension of patch. Therefore, the comparison of different proposed algorithms is analyzed to obtain higher characteristics for antenna design. Their results are compared with each other in HFSS 13.0 software. The antenna with the most suitable return loss, bandwidth and operating frequency is selected to be used in antenna design.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Liu L, Cheung SW, Yuk TI (2013) Compact MIMO antenna for portable devices in UWB applications. IEEE Trans Antennas Propag 61:4257–4264

    Article  Google Scholar 

  2. Chiu CC, Ho MH, Liao SH (2013) PSO and APSO for optimizing coverage in indoor UWB communication system. Int J RF Microw Comput Aided Eng 23:300–308

    Article  Google Scholar 

  3. Behdad N, Sarabandi K (2005) A compact antenna for ultrawide-band applications. IEEE Trans Antennas Propag 53:2185–2192

    Article  Google Scholar 

  4. Nazli H, Bicak E, Turetken B, Sezgin M (2010) An improved design of planar elliptical dipole antenna for UWB applications. IEEE Antennas Wirel Propag Lett 9:264–267

    Article  Google Scholar 

  5. Ojaroudi N, Ojaroudi M, Ghadimi N (2012) UWB omnidirectional square monopole antenna for use in circular cylindrical microwave imaging systems. IEEE Antennas Wirel Propag Lett 11:1350–1353

    Article  Google Scholar 

  6. Liu L, Cheung SW, Azim R, Islam MT (2011) A compact circularring antenna for ultra-wideband applications. Microw Opt Technol Lett 53:2283–2288

    Article  Google Scholar 

  7. Thomas P, Gopikrishna M, Aanandan CK, Mohanan P, Vasudevan K (2010) A compact pentagonal monopole antenna for portable UWB systems. Microw Opt Technol Lett 52:2390–2393

    Article  Google Scholar 

  8. Ray KP (2008) Design aspects of printed monopole antennas for ultrawide band applications. Int J Antennas Propag 2008:1–8

    Article  Google Scholar 

  9. Fakharian MM, Rezaei P (2012) Parametric study of UC-PBG structure in terms of simultaneous AMC and EBG properties and its applications in proximity-coupled fractal patch antenna. Int J Eng Trans A Basics 25:389–396

    Google Scholar 

  10. Fakharian MM, Rezaei P (2012) Numerical analysis of mushroomlike and uniplanar EBG structures utilizing spin sprayed Ni (–Zn)–Co ferrite films for planar antenna. Eur J Sci Res 73:41–51

    Google Scholar 

  11. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295(1):395–406

    Article  Google Scholar 

  12. Gu B, Sun X, Sheng VS (2016) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2016.2544779

    Google Scholar 

  13. Zheng Y, Jeon B, Xu D, Wu QM, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):961–973

    Google Scholar 

  14. Li H, Liao X, Li C et al (2011) Edge detection of noisy images based on cellular neural networks. Commun Nonlinear Sci Numer Simul 16(9):3746–3759

    Article  MathSciNet  MATH  Google Scholar 

  15. Pan Z, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast 61(2):166–176

    Article  Google Scholar 

  16. Warner B, Misra M (1996) Understanding neural networks as statistical tools. Am Stat 50(4):284–293S

    Google Scholar 

  17. Masters Timothy (1993) Practical neural network recipes in C++. Academic Press Inc, London

    MATH  Google Scholar 

  18. Kennedy J, Eberhart RC (1995) Particle swarm optimization Proceeding IEEE international conference on neural networks, 1995, 1942-1948

  19. Poor H (1985) An introduction to signal detection and estimation. Springer-Verlag, New York

    MATH  Google Scholar 

  20. Vilovic I, Burum N (2014) Optimization of feed position of circular microstrip antenna using PSO. The 8th European conference on antennas and propagation (EuCAP 2014)

  21. Storn R, Price K (1997) Differential Evolution- a simple and efficient heuristic for global optimization over continuous spaces. J Global Opt 11:341–359

    Article  MathSciNet  MATH  Google Scholar 

  22. Deb A, Roy JS, Gupta B (2012) Design of short-circuited microstrip antenna using differential evolution algorithm. Microw Rev 18(N0):3

    Google Scholar 

  23. Li JY, Guo JL (2009) Optimization technique using differential evolution for Yagi-Uda antennas. J Electromagn Waves Appl 23(4):449–461

    Article  Google Scholar 

  24. Aksoy E, Afacan E (2009) Planar antenna pattern nulling using differential evolution algorithm. AEU Int J Electro Commun 63:116–122

    Article  Google Scholar 

  25. Tvrdik J (2007) Differential evolution with competitive setting of control parameters. Task Q 11(1–2):169–179

    Google Scholar 

  26. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  27. Guptai P, Rana KPS, Kumar V, Nair SS (2015) Development of a grey wolf optimizer toolkit in LabVIEW™,” 9th international conference on futuristic trend in computation analysis and knowledge management, 2015

  28. Dogan B, Olmez T (2015) A new metaheuristic for numerical function optimization: vortex Search algorithm. Inf Sci 293:125–145

    Article  Google Scholar 

  29. Dogan B, Olmez T (2015) Analog filter group delay optimization using the vortex search algorithm. 23rd signal processing and communications application conference, Malatya, 2015

  30. Dogan B, Olmez T (2015) Modified Off-lattice AB Model for Protein Folding Problem Using the Vortex Search Algorithm. Int J Mach Learn Comput 5(4):329

    Article  Google Scholar 

Download references

Acknowledgments

I would like to thank Scientific Research Projects (BAP) coordinating office of Selcuk University, and the Scientific & Technological Research Council of Turkey (TÜBİTAK) for their valuable supports.

Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Engineering Faculty, Selcuk University, Konya, Turkey

    Umut Ozkaya & Levent Seyfi

Authors
  1. Umut Ozkaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Levent Seyfi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Levent Seyfi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ozkaya, U., Seyfi, L. A comparative study on parameters of leaf-shaped patch antenna using hybrid artificial intelligence network models. Neural Comput & Applic 29, 35–45 (2018). https://doi.org/10.1007/s00521-016-2620-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2620-1

Keywords

Search

Navigation