Skip to main content
SpringerLink
Log in

Modeling of double ridge waveguide using ANN

  • Research Article
  • Published:
Frontiers of Electrical and Electronic Engineering

Abstract

The ridge waveguide is useful in various microwave applications because it can be operated at a lower frequency and has lower impedance and a wider mode separation than a simple rectangular waveguide. An accurate model is essential for the analysis and design of ridge waveguide that can be obtained using electromagnetic simulations. However, the electromagnetic simulation is expensive for its high computational cost. Therefore, artificial neural networks (ANNs) become very useful especially when several model evaluations are required during design and optimization. Recently, ANNs have been used for solving a wide variety of radio frequency (RF) and microwave computer-aided design (CAD) problems. Analysis and design of a double ridge waveguide has been presented in this paper using ANN forward and inverse models. For the analysis, a simple ANN forward model is used where the inputs are geometrical parameters and the outputs are electrical parameters. For the design of RF and microwave components, an inverse model is used where the inputs are electrical parameters and the outputs are geometrical parameters. This paper also presents a comparison of the direct inverse model and the proposed inverse model.

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

Similar content being viewed by others

References

  1. Helszajn J. Ridge Waveguides and Passive Microwave Components. Institution of Engineering and Technology, 2001

  2. Cohn S B. Properties of ridge wave guide. Proceedings of the IRE, 1947, 35(8): 783–788

    Article  Google Scholar 

  3. Hopfer S. The design of ridged waveguides. IRE Transactions on Microwave Theory and Techniques, 1955, 3(5): 20–29

    Article  Google Scholar 

  4. Saad A M K. A unified ridge structure for evanescent mode wideband harmonic filters: Analysis and applications. In: Proceedings of the 17th European Microwave Conference. 1987, 157–162

  5. Saad A M K. Novel low pass harmonic filters for satellite application. In: Proceedings of IEEE MTT-S International Microwave Symposium Digest. 1984, 292–294

  6. Saad A M K, Miller J D, Mitha A, Brown R. Analysis of antipodal ridge waveguide structure and application on extremely wide stop band low pass filter. In: Proceedings of IEEE MTT-S International Microwave Symposium Digest. 1986, 361–363

  7. Saad A M K, Mitha A, Brown R. Evanescent mode-serrated ridge waveguide bandpass harmonic filters. In: Proceedings of the 16th European Microwave Conference. 1986, 287–291

  8. Gipprich J, Stevens D, Hageman M, Piloto A, Zaki K A, Rong Y. Embedded waveguide filters for microwave and wireless applications using cofired ceramic technologies. In: Proceedings of International Symposium on microelectronics. 1998

  9. Rong Y, Zaki K A, Hageman M, Stevens D, Gipprich J. Lowtemperature cofired ceramic (LTCC) ridge waveguide bandpass chip filters. IEEE Transactions on Microwave Theory and Techniques, 1999, 47(12): 2317–2324

    Article  Google Scholar 

  10. Zhang Q J, Gupta K C, Devabhaktuni V K. Artificial neural networks for RF and microwave design — from theory to practice. IEEE Transactions on Microwave Theory and Techniques, 2003, 51(4): 1339–1350

    Article  Google Scholar 

  11. Haykin S. Neural Networks: A Comprehensive Foundation. New York, USA: Macmillan College Publishing Company, 1994

    MATH  Google Scholar 

  12. Zhang Q J, Gupta K C. Neural Networks for RF and Microwave Design. Artech House, 2000

  13. Christodoulou C G, Georgiopoulos M. Application of Neural Networks in Electromagnetics. Artech House, 2001

  14. Guney K, Yildiz C, Kaya S, Turkmen M. Artificial neural networks for calculating the characteristic impedance of air-suspended trapezoidal and rectangular-shaped micro shield lines. Journal of Electromagnetic Waves and Applications, 2006, 20(9): 1161–1174

    Article  Google Scholar 

  15. Jin L, Ruan C L, Chun L Y. Design E-plane bandpass filter based on EM-ANN model. Journal of Electromagnetic Waves and Applications, 2006, 20(8): 1061–1069

    Article  Google Scholar 

  16. Mohamed M D A, Soliman E A, El-Gamal M A. Optimization and characterization of electromagnetically coupled patch antennas using RBF neural networks. Journal of Electromagnetic Waves and Applications, 2006, 20(8): 1101–1114

    Article  Google Scholar 

  17. Thomas V, Gopakumar C, Yohannan J, Lonappan A, Bindu G, Praveen Kumar A V, Hamsakutty V, Mathew K T. A novel technique for localizing the scatterer in inverse profiling of two dimensional circularly symmetric dielectric scatterers using degree of symmetry and neural networks. Journal of Electromagnetic Waves and Applications, 2005, 19(15): 2113–2121

    Article  Google Scholar 

  18. Lakshmi Narayana J, Sri Rama Krishna K, Vanajakshi B. Neural network models for non-linear devices and circuits. In: Proceedings of International Conference on Computer Communication and Control. 2006, 21–25

  19. Lakshmi Narayana J, Sri Rama Krishna K, Pratap Reddy L. Neural network training algorithms for RF and microwave applications. In: Proceedings of International Conference on Advanced Computing and Communication Technologies. 2007, 496–501

  20. Lakshmi Narayana J, Sri Rama Krishna K, Pratap Reddy L. ANN structures for RF and microwave applications. In: Proceedings of International Conference on Recent Applications of Soft Computing in Engineering and Technology. 2007, 91–98

  21. Hornik K, Stinchcombe M, White H. Multilayer feed forward networks are universal approximators. Neural Networks, 1989, 2(5): 359–366

    Article  Google Scholar 

  22. Devabhaktuni V K, Yagoub M C E, Fang Y, Xu J, Zhang Q J. Neural networks for microwave modeling: Model development issues and nonlinear modeling techniques. International Journal of RF and Microwave Computer-Aided Engineering, 2001, 11(1): 4–21

    Article  Google Scholar 

  23. Creech G L, Paul B J, Lesniak C D, Jenkins T J, Calcatera M C. Artificial neural networks for fast and accurate EM-CAD of microwave circuits. IEEE Transactions on Microwave Theory and Techniques, 1997, 45(5): 794–802

    Article  Google Scholar 

  24. Vai M M, Wu S, Li B, Prasad S. Reverse modeling of microwave circuits with bidirectional neural network models. IEEE Transactions on Microwave Theory and Techniques, 1998, 46(10): 1492–1494

    Article  Google Scholar 

  25. Selleri S, Manetti S, Pelosi G. Neural network applications in microwave device design. International Journal of RF and Microwave Computer-Aided Engineering, 2002, 12(1): 90–97

    Article  Google Scholar 

  26. Kabir H, Wang Y, Yu M, Zhang Q J. Neural network inverse modeling and applications to microwave filter design. IEEE Transactions on Microwave Theory and Techniques, 2008, 56(4): 867–879

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics & Communication Engineering, St. Ann’s College of Engineering and Technology, Chirala, A.P., India

    J. Lakshmi Narayana, K. Sri Rama Krishna, L. Pratap Reddy & G. V. Subrahmanyam

Authors
  1. J. Lakshmi Narayana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Sri Rama Krishna
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. Pratap Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. V. Subrahmanyam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Lakshmi Narayana.

About this article

Cite this article

Lakshmi Narayana, J., Sri Rama Krishna, K., Pratap Reddy, L. et al. Modeling of double ridge waveguide using ANN. Front. Electr. Electron. Eng. 7, 299–307 (2012). https://doi.org/10.1007/s11460-012-0200-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11460-012-0200-4

Keywords

Search

Navigation