Back propagation neural network method of solution of normal fat dipole and truncated conical grounded monopole and optimization by genetic algorithm

Authors

  • C. D. Gupta Indian Institute of Technology, Kanpur, India

DOI:

https://doi.org/10.1109/ICATT.2007.4425159

Keywords:

backpropagation, conical antennas, dipole antennas, genetic algorithms, monopole antennas, neural nets, statistical analysis, telecommunication computing

Abstract

In order to regularize the software by Back Propagation Neural Network (BPNN) two types of dipoles viz. normal fat dipoles as treated in many handbooks and truncated conical dipoles are selected in this paper. The first type is essentially to find out the feasibility of BPNN software to be applied for grounded truncated conical monopole. The second case is a semi-empirical approach that has been developed, where from the optimal dimensions are selected by means of genetic algorithm.

References

JASIK, H. Antenna Engineering Handbook. McGraw Hill Publisher.

GUPTA, CHINMOY DAS. Broad based tutorial paper. Proc. of IEEE Int. Symp. on A&P, 8-13 July 2001, Boston. 2001.

ICATT 2007 cover

Published

2007-09-22

Issue

2007: ICATT’2007 - Proc. of VI Int. Conf. on Antenna Theory and Techniques

Section

Analytical and numerical techniques