Application of ANNs in Evaluation of Microwave Pyramidal Absorber Performance
Keywords:
Application of ANNs in Evaluation of Microwave Pyramidal Absorber PerformanceAbstract
To evaluate the overall anechoic chamber performance it is necessary to determine reflectivity of the absorbers. As manufacturer specifications usually give only information about frequency dependent reflection coefficient at normal incidence of EM waves, a time-consuming electromagnetic analysis is necessary to calculate the reflection coefficient at off-normal incident angles. In this paper, an efficient alternative approach to obtain the reflection coefficient at offnormal incidence is proposed. It is based on artificial neural networks trained to model the absorber reflectivity dependence on the frequency and incident angle of horizontally and vertically polarized electromagnetic waves. The model has been developed for pyramidal absorbers at low microwave frequencies (0.4 GHz–1 GHz).
Downloads
References
B. K. Chung and H. T. Chuah, “Modeling of RF
Absorber for Application in the Design of
Anechoic Chamber,” Progress in
Electromagnetics Research ( PIER), vol. 43, pp.
-285, 2003.
B. B . Janić and B. M. Kolundžija, “RF Absorber
Reflectivity Evaluation using RSC Calculation,”
IEEE AP-S International Symposium 2007,
Honolulu, Hawaii, USA, pp. 6043-6046, June
A. Khajehpour and S. A. Mirtaheri “Analysis of
Pyramid EM Wave Absorber by FDTD Method
and Comparing with Capacitance and
Homogenization Methods,” PIERS, vol. 3, pp.
-131, 2008.
J. T. Gear, “Microwave Absorbers Manage
Military Electronics RF Interference,” Defense
Electronics, pp. 6-9, 2004.
H. Anzai, M. Saikawa, Y. Naito, and T.
Mizumoto, “The Equivalent Representation of
Pyramidal Absorbers and its Application to the
Analysis of Electromagnetic Wave Absorber’s
Characteristics,” Proceedings of the IEEE
International Symposium on EMC, Atlanta, USA,
pp. 563-567, 1995.
C. L. Holloway, P. M. McKenna, R. A. Dalke, R.
A. Perala, and C. L. Devor, “Time-Domain
Modeling, Characterization, and Measurements of
Anechoic and Semi-Anechoic Electromagnetic
Test Chambers,” IEEE Trans. on EMC, vol. 44,
no. 1, pp. 102-118, 2002.
E. Kuester and C. A. Holloway, “A Low-
Frequency Model for Wedge or Pyramid Absorber
Arrays-I: Theory,” IEEE Trans. on EMC, vol. 36,
no. 4, pp. 300-307, 1994.
S. Kent and M. Kartal, “Dielectric Absorber
Design for Wide Band–Wide Oblique Incidence
Angle using Genetic Algorithm,” Int. J. Electron.
Commun. (AEÜ), vol. 61, pp. 398-404, 2007.
E. Michielssen, J. M. Sajer, S. Ranjithan, and R.
Mittra, “Design of Lightweight, Broad-Band
Microwave Absorbers using Genetic Algorithms,”
IEEE Trans. on MTT, vol. 41, no. 6/7, pp. 1024-
, 1993.
S. Kent and M. Kartal, “Genetic Algorithm
Approach on Pyramidal Dielectric Absorbers,”
Int. J. of RF and Microwave Computer-Aided
Engineering, vol. 18, no. 3, pp. 286-294, 2008.
T. Kaufmann, K. Sankaran, and C. Fumeaux, “A
Review of Perfectly Matched Absorbers for the
Finite-Volume Time-Domain Method,” Applied
Computational Electromagnetic Society (ACES)
Journal, vol. 23 no. 3, pp. 184-192, 2008.
E. A. Hashish and S. M. Eid, “ Design of
Wideband Planar Absorbers using Composite
Materials,” Applied Computational
Electromagnetic Society ( ACES) Journal, vol. 24,
no. 4, pp. 413-418, 2009.
AGATONOVIC, ET. AL.: APPLICATION OF ANNS IN EVALUATION OF MICROWAVE PYRAMIDAL ABSORBER PERFORMANCE
Q. J. Zhang and K. C. Gupta, Neural Networks for
RF and Microwave Design, Artech House, Inc.
Norwood, MA, USA, 2000.
C. Christodoulou and M. Gerogiopoulos,
Applications of Neural Networks in
Electromagnetics, Artech House, Inc. Norwood,
MA, USA, 2000.
S. S. Gultekin, K. Guney, and S. Sagiroglu,
“Neural Networks for the Calculation of
Bandwidth of Rectangular Microstrip Antennas,“
Applied Computational Electromagnetic Society
(ACES) Journal, vol. 18, no. 2, pp. 110-120, 2003.
J. E. Rayas-Sanchez, “EM-Based Optimization of
Microwave Circuits using Artificial Neural
Networks: The State-of-the-Art,” IEEE Trans.
Microw. Theory Tech., vol. 52, no. 1, pp. 420-435,
Jan. 2004.
V. Marković and Z. Marinković, "HEMT Noise
Neural Model Based on Bias Conditions,"
COMPEL, Emerald, vol. 23, no. 2, pp. 426-435,
L. Zhang, Q. -J. Zhang, and J. Wood, “Statistical
Neuro-Space Mapping Technique for Large-
Signal Modeling of Nonlinear Devices,” IEEE
Trans. on MTT, vol. 56, no. 11, pp. 2453-2467,
R. Ghayoula, N. Fadlallah, A. Gharsallah, and M.
Rammal, “Design, Modeling, and Synthesis of
Radiation Pattern of Intelligent Antenna by
Artificial Neural Networks,” Applied
Computational Electromagnetic Society (ACES)
Journal, vol. 23, no. 4, pp. 336-344, 2008.
H. Kabir, Y. Cao, and Q. Zhang, “ Advances of
Neural Network Modeling Methods for
RF/Microwave Applications,” Applied
Computational Electromagnetic Society (ACES)
Journal, vol. 25, no. 5, pp. 423-432, 2010.
Z. Marinković, G. Crupi, A. Caddemi, and V.
Marković, “Microwave FinFET Modeling Based
on Artificial Neural Networks Including Lossy
Silicon Substrate,” Microelectronic Engineering,
vol. 88, no. 10, pp. 3158-3163, 2011.
WIPL-D Pro v7.1, “Software and User’s Manual,”
WIPL-D d.o.o., Belgrade, 2009.
