Pole discontinuity removal using artificial neural networks for microstrip antenna design
This article presents the use of artificial neural networks for the evaluation of integrals with finite number of pole singularities while formulating the integral equation for the electric surface current density. A feed-forward single-layer back-propagated artificial neural network (ANN) model has...
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Veröffentlicht in: | International journal of electronics 2011-12, Vol.98 (12), p.1711-1720 |
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Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
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Online-Zugang: | Volltext |
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Zusammenfassung: | This article presents the use of artificial neural networks for the evaluation of integrals with finite number of pole singularities while formulating the integral equation for the electric surface current density. A feed-forward single-layer back-propagated artificial neural network (ANN) model has been trained to approximate the discontinuous integrand function. Generation of a soft continuous function obtained from the ANN model and closed-loop expressions for the evaluation of the integrals are presented. The proposed technique is applied to compute the input impedance of microstrip antenna and results have been compared with IE3D. Integration has been performed using n-point Gaussian quadrature rule for evaluating the reaction matrix. |
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ISSN: | 0020-7217 1362-3060 |
DOI: | 10.1080/00207217.2011.609970 |