Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation. (23rd October 2013)

• Record Type: Journal Article
• Authors: Dhaliwal, Balwinder S.; Pattnaik, Shyam S.
• Other Names: Kisi Ozgur Academic Editor.
• Is Part Of: Advances in artificial neural systems. (2013)
• Publication Date: 2013-10-23
• Subjects: Neural networks (Computer science) -- Periodicals; Neural networks (Computer science); Periodicals; Electronic journals; 006.32
• DOI: 10.1155/2013/560969
• Languages: English
• ISSNs: 1687-7594

Abstract

Abstract : Artificial neural networks due to their general-purpose nature are used to solve problems in diverse fields. Artificial neural networks (ANNs) are very useful for fractal antenna analysis as the development of mathematical models of such antennas is very difficult due to complex shapes and geometries. As such empirical approach doing experiments is costly and time consuming, in this paper, application of artificial neural networks analysis is presented taking the Sierpinski gasket fractal antenna as an example. The performance of three different types of networks is evaluated and the best network for this type of applications has been proposed. The comparison of ANN results with experimental results validates that this technique is an alternative to experimental analysis. This low cost method of antenna analysis will be very useful to understand various aspects of fractal antennas.