Vol. 7, Issue 3, Part G (2021)
Study of artificial neural networks for broadband antenna based on a parametric frequency model
Study of artificial neural networks for broadband antenna based on a parametric frequency model
Author(s)
Alpana Kumari
Abstract
In this paper neural network (ANN) is proposed to predict the input impedance of a broadband antenna as a function of its geometric parameters. The input resistance of the antenna is first parameterized by a Gaussian model, and the ANN is constructed to approximate the nonlinear relationship between the antenna geometry and the model parameters. A hybrid gradient descent and particle swarm optimization method is used to train the neural network. The antenna structure is then optimized for broadband operation via a genetic algorithm that uses input impedance estimates provided by the trained ANN in place of brute-force electromagnetic computations. It is found that the required number of electromagnetic computations in training the ANN is ten times lower than that needed during the antenna optimization process.
How to cite this article:
Alpana Kumari. Study of artificial neural networks for broadband antenna based on a parametric frequency model. Int J Appl Res 2021;7(3):461-463.