Table of Contents
ISRN Electronics
Volume 2014, Article ID 296105, 6 pages
Research Article

Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks

Department of Electronics and Communication Engineering, National Institute of Technology, Patna 800005, India

Received 28 January 2014; Accepted 6 March 2014; Published 27 March 2014

Academic Editors: H. L. Hartnagel, L.-F. Mao, and Y.-H. Wang

Copyright © 2014 Taimoor Khan and Asok De. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Over the past decade, artificial neural networks have emerged as fast computational medium for predicting different performance parameters of microstrip antennas due to their learning and generalization features. This paper illustrates a neural network model for instantly predicting the resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual-frequency operation of slotted microstrip antennas with air-gap. The proposed neural model is valid for any arbitrary slot-dimensions and inserted air-gap within their specified ranges. A prototype is fabricated using Roger’s substrate and its performance is measured for validation. A very good agreement is achieved in simulated, predicted, and measured results.