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. 1. Introduction Artificial neural networks (ANNs) have been used as efficient tools for modeling and prediction in almost all disciplines. The use of ANN has become widely accepted in antenna design and analysis applications. This is evident from the increasing number of publications in research/academic journals [1–8]. Angiulli and Versaci proposed a technique to evaluate the resonant frequency of microstrip antennas using neuro-fuzzy networks [1]. The use of ANN for the design of rectangular patch antenna is explained in [2]. Applications of ANN in various types of antennas and antenna arrays are explained in [3]. Neog et al. [4] have used a tunnel based ANN for the parameter calculation of the wideband microstrip antenna. Lebbar et al. [5] employed a geometrical methodology based ANN for the design of a compact broadband microstrip antenna. In [6] the authors proposed an ANN to predict the input impedance of a broadband antenna as a function of its geometric parameters. Guney and Sarikaya [7] presented a hybrid method based on a combination of ANN and fuzzy inference system to calculate simultaneously the resonant frequencies of various microstrip antennas of regular geometries. An equilateral triangular microstrip antenna has been designed using a particle swarm optimization driven radial basis function neural networks by [8]. However, the use of ANN in analysis & design of fractal antennas is at very early stage. A limited number of literatures are available in this field of antennas [9–12]. In this paper, the performance of three different ANNs on Sierpinski gasket fractal antenna analysis is investigated by means of two aspects: mean absolute error (MAE) and
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