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Performance parameters prediction of slotted microstrip antennas with modified ground plane using support vector machine

  • Chandan Roy (a1), Taimoor Khan (a1) and Binod Kumar Kanaujia (a2)

Abstract

Artificial neural networks (ANNs) have acquired enormous importance in computing of the performance parameters of microstrip antennas due to their generalized and adaptive features. However, recently the concept of support vector machines (SVMs) has become very much popular in performance parameters computation due to several attractive features over ANNs. Specifically, SVMs outreach ANNs noticeably in terms of execution time. Likewise, ANNs are having multiple local minima problem, whereas a global and unique solution is provided by SVMs. In this paper, several performance parameters like: resonant frequency, gain, directivity, and radiation efficiency of slotted microstrip antennas with modified ground plane are computed with the help of SVM formulation. Comparisons of different parameters of simulated and computed values are illustrated. The achieved radiation patterns at particular resonant frequency in different planes are included as well. A prototype of the optimized antenna is also fabricated and characterized. A good agreement is attained among the computed, simulated, and measured results.

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Corresponding author

Corresponding author: T. Khan Email: ktaimoor@gmail.com

References

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Keywords

Performance parameters prediction of slotted microstrip antennas with modified ground plane using support vector machine

  • Chandan Roy (a1), Taimoor Khan (a1) and Binod Kumar Kanaujia (a2)

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