Artificial Neural Network based Parameter Estimation and Design Optimization of Loop Antenna
Abstract:Artificial Neural Network (ANN)s are best suited for
prediction and optimization problems. Trained ANNs have found
wide spread acceptance in several antenna design systems. Four
parameters namely antenna radiation resistance, loss resistance, efficiency,
and inductance can be used to design an antenna layout though
there are several other parameters available. An ANN can be trained
to provide the best and worst case precisions of an antenna design
problem defined by these four parameters. This work describes the
use of an ANN to generate the four mentioned parameters for a loop
antenna for the specified frequency range. It also provides insights
to the prediction of best and worst-case design problems observed
in applications and thereby formulate a model for physical layout
design of a loop antenna.
 S. Haykin. "Neural Networks: A Comprehensive Foundation". Pearson
Education, 2nd Ed., New Delhi, 2003.
 V. R. Gupta and N. Gupta. "An Artificial Neural Network Model for Feed
Position of the Microstrip Antenna", Electronika Ir Elektrotechnika,
 R. K. Mishra, and A. Patnaik, . "Neural Network Based CAD Model for
the Design of Square-Patch Antenna". IEEE Transactions on Antenna
And Propagation, December, 1998.
 K. Siakavara . "Artificial Neural Network Employment in the Design of
Multilayred Microstrip Antenna with Specified Frequency Operation",
PIERS Proceedings, pp. 27 − 30, Prague, August, 2007.
 A. A. Eldek . " Design Of Double Dipole Antenna With Enhanced
Usable Bandwidth for Wideband Phased Array Application", Progress
In Electromagnetics Research, PIER, 2006.
 C. A. Balanis. "Antenna Theory, Analysis and Design", John Willey
and Sons INC, New York, 2005
 G. S. N. Raju. "Antenna And Wave Propagation", Pearson Education,
1st Ed., New Delhi, 2008