Table of Contents
Chinese Journal of Engineering
Volume 2014, Article ID 924927, 11 pages
Research Article

Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

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

Received 11 December 2013; Accepted 16 January 2014; Published 26 February 2014

Academic Editors: J. Deng, Z. Ji, and L. Li

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 one decade, neural networks-based modeling has been used for computing different performance parameters of microstrip antennas because of learning and generalization features. Most of the created neural models are based on software simulation. As the neural networks show massive parallelism inherently, a parallel hardware needs to be created for creating faster computing machine by taking the advantages of the parallelism of the neural networks. This paper demonstrates a generalized neural networks model created on field programmable gate array- (FPGA-) based reconfigurable hardware platform for computing different performance parameters of microstrip antennas. Thus, the proposed approach provides a platform for developing low-cost neural network-based FPGA simulators for microwave applications. Also, the results obtained by this approach are in very good agreement with the measured results available in the literature.