Hostname: page-component-8448b6f56d-wq2xx Total loading time: 0 Render date: 2024-04-23T21:42:05.668Z Has data issue: false hasContentIssue false
  • English
  • Français

Accurate modeling and optimization of microwave circuits and devices using adaptive neuro-fuzzy inference system

Published online by Cambridge University Press:  01 July 2011

Youssef Harkouss
Youssef Harkouss*
Affiliation:
Lebanese University-Faculty of Engineering-Branch III, P.O. Box 14, 6573, Al Hadath, Beirut, Lebanon. Phone: +961 3608734
*
Corresponding author: Y. Harkouss Email: harkouss@ul.edu.lb
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, an accurate neuro-fuzzy-based model is proposed for efficient computer-aided design (CAD) modeling and optimization of microwave circuits and devices. The adaptive neuro-fuzzy inference system (ANFIS) approach is used to determine the scattering parameters of a microstrip filter and is applied to the optimization design of this microstrip filter. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of artificial neural networks. The neuro-fuzzy model has been trained and tested with different sets of input/output data. Finally, different results, which confirm the validity of the proposed model, are reported.

Keywords

Adaptive neuro-fuzzy inference system (ANFIS)ModelingOptimizationMicrowave devicesMicrostripComputer-aided design (CAD)
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 3 , Issue 6 , December 2011 , pp. 637 - 645
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1]Hoffmann, R.K.: Handbook of Microwave Integrated Circuits, Artech House, Nonvood, MA, USA, 1987.Google Scholar
[2]Gupta, K.C.; Garg, R.; Bahl, I.J.: Microstrip Lines and Slot-lines, 2nd ed., Artech House, Nonvood, MA, USA, 1996.Google Scholar
[3]Djordjevic, A.R.; Harrington, R.F.; Sarkar, T.K.: Matrix Parameters of Multiconductor Transmissions Lines, Artech House, Nonvood, MA, USA, 1989.Google Scholar
[4]Miraftab, V.; Mansour, R. R.: Computer-aided tuning of microwave filters using fuzzy logic, in IEEE MTT-S Digest, 2002, 1117–1120.Google Scholar
[5]Koziel, S.; Bandler, J.W.: A space-mapping approach to microwave device modeling exploiting fuzzy systems. IEEE Trans. Microw. Theory Tech., 55(12) (2007), 2539–2547.CrossRefGoogle Scholar
[6]Harkouss, Y.; Ngoya, E.; Rousset, J.; Argollo, D.: Accurate radial wavelet neural-network model for efficient CAD modeling of microstrip discontinuities. IEE Proc. – Microw. Antennas Propag., 147(4) (2000), 277–283.Google Scholar
[7]Zhang, Q.J.; Gupta, K.C.; Devabhaktuni, V. K.: Artificial neural networks for RF and microwave design – from theory to practice. IEEE Trans. Microw. Theory Tech., 51(4) (2003), 1339–1350.CrossRefGoogle Scholar
[8]Jang, J.-S.R.: ANFIS: adaptative-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern., 23(3) (1993), 665–685.CrossRefGoogle Scholar
[9]Jang, J.-S.R.; Sun, C.T.; Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall, Upper Saddle River, NJ, 1997.Google Scholar
[10]Yildiz, C.; Guney, K.; Turkmen, M.; Kaya, S.: Analysis of conductor-backed coplanar waveguides using adaptive-network-based fuzzy inference system models. Microw. Opt. Technol. Lett., 51(2) (2009), 439–455.CrossRefGoogle Scholar
[11]Ubeyli, E.D.; Guler, I.: Adaptive neuro-fuzzy inference system to compute quasi-TEM characteristic parameters of microshield lines with practical cavity sidewall profiles. Neurocomputing, 70(1–3) (2006), 196–204.CrossRefGoogle Scholar
[12]Rahouyi, E.B.; Hinojosa, J.; Garrigos, J.: Neuro-fuzzy modeling techniques for microwave components, IEEE Microw. Wirel. Compo. Lett. 16(2) (2006), 72–74.CrossRefGoogle Scholar
[13]Hinojosa, J.; Dome'nech-Asensi, G.: Space-mapped neuro-fuzzy optimization for microwave device modeling. Microw. Opt. Technol. Lett., 49(6) (2007), 1328–1334.CrossRefGoogle Scholar
[14]Yildiz, C.; Guney, K.; Turkmen, M.; Kaya, S.: Adaptive neuro-fuzzy models for the quasi-static analysis of microstrip line. Microw. Opt. Technol. Lett., 50(5) (2008), 1191–1196.Google Scholar
[15]Gaoua, S.L.; Ji Cheng, Z.; Mohammadi, F.A.; Yagoub, M.C.E.: Fuzzy neural-based approaches for efficient RF/microwave transistor modeling. Int. J. RF Microw. CAE, 19(1) (2009), 128–139.CrossRefGoogle Scholar
[16]Serenade SV, Ansoft Corporation, version 8.5, Pittsburgh.Google Scholar