Table of Contents
International Scholarly Research Notices
Volume 2014, Article ID 957469, 9 pages
Research Article

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

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

Received 20 March 2014; Revised 9 July 2014; Accepted 12 July 2014; Published 29 October 2014

Academic Editor: Herbert Homeier

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.


In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.