Skip to main content

The Use of Artificial Neural Network in the Design of Metamaterials

  • Conference paper
Intelligent Data Engineering and Automated Learning - IDEAL 2012 (IDEAL 2012)

Abstract

This paper presents an analysis of the resonant characteristics of a composite medium based on a periodic array of interspaced conducting nonmagnetic split ring resonators and continuous thin wires. The medium exhibits simultaneously negative values of effective permeability and permittivity within a microwave frequency band, characterizing a metamaterial. An analysis using Artificial Neural Networks is performed to obtain the permeability and permittivity as a function of resonant frequencies for a given geometrical dimensions of the metamaterial, for an optimization of the development medium.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Veselago, V.G.: The electrodynamics of substances with simultaneously negative values of ε and μ. Sov. Phys.—Usp. 10, 509–514 (1968)

    Article  Google Scholar 

  2. Pendry, J.B., Holden, A.J., Robbins, D.J., Stewart, W.J.: Magnetism from conductors and enhanced nonlinear phenomena. IEEE Trans. Microw. Theory Tech. 47(11), 2075–2081 (1999)

    Article  Google Scholar 

  3. Vasconcelos, C.F.L., Albuquerque, M.R.M.L., Silva, S.G., Oliveira, J.R.S., D’Assunção, A.G.: Full-wave analysis of annular ring microstrip antenna on metamaterial. IEEE Transactions on Magnetics 47(5) (2011)

    Google Scholar 

  4. Smith, D.R., Padilla, W.J., Vier, D.C., Nemat-Nasser, S.C., Schultz, S.: Composite Medium with Simultaneously Negative Permeability and Permittivity. Appl. Phys. Lett. 84(18), 4184–4187 (2000)

    Article  Google Scholar 

  5. Smith, D.R., Vier, D.C., Kroll, N., Schultz, S.: Direct calculation of permeability and permittivity for a left-handed metamaterial. Appl. Phys. Lett. 77(14), 2246–2248 (2000)

    Article  Google Scholar 

  6. Itoh, T., Caloz, C.: Electromagnetic metamaterials: transmission line theory and microwave applications. John Wiley&Sons, Inc., New Jersey (2006)

    Google Scholar 

  7. Haykin, S.: Neural networks: a comprehensive foundation. Macmillan College Publishing Company, New York (1994)

    MATH  Google Scholar 

  8. Silva, P.H.D.F., Cruz, R.M.S., D’Assunção, A.G.: Neuromodeling and natural optimization of nonlinear devices and circuits. In: Temel, T. (ed.) System and Circuit Design for Biologically-Inspired Intelligent Learning. IGI Global, Hershey (2010)

    Google Scholar 

  9. Zhang, Q.J., Gupta, C.: Neural networks for RF and microwaves design. Artech House, Norwood (2000)

    Google Scholar 

  10. Riedmiller, M., Braun, H.: A direct adaptative method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of IEEE International Conference on Neural Networks, San Francisco, USA, pp. 586–591 (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vasconcelos, C.F.L., Rêgo, S.L., Cruz, R.M.S. (2012). The Use of Artificial Neural Network in the Design of Metamaterials. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_65

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32639-4_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32638-7

  • Online ISBN: 978-3-642-32639-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics