An ANN Solution for Generalized Sierpinski Fractal Antenna

Anuradha, .; Rani, Shweta; Kakkar, Sushil

doi:10.1051/matecconf/20165701024

Cited by 2 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the initial step, data samples are categorized into training (approximately 70%), testing (15%), and validation (15%) sets. The percentage allocation may vary based on specific application requirements [18][19][20]. Subsequently, the network size is determined, specifying the number of hidden layers and neurons in each layer.…”

Section: Antenna Design Using Neural Network Modelmentioning

confidence: 99%

Design and Optimization of Sierpinski Carpet Fractal Antenna Using Artificial Neural Network (Ann) and High Frequency Structure Simulator (Hfss) Computational Techniques

Patil,

Vanjerkhede

2023

ecb

View full text Add to dashboard Cite

In the realm of modern wireless communication systems, the demand for compact, efficient, and multifunctional antennas has led to the exploration of novel design paradigms. In the field of wireless communication with constantly evolving antenna technology the need for antennas with enhanced bandwidth, gain and low profile is tremendously increasing. Fractal antennas have garnered significant attention due to their unique characteristics that lead to improved performance and miniaturization with broadband/multiband characteristic in modern wireless communication systems. This paper presents the design and optimization of a Sierpinski Carpet fractal antenna using advanced computational techniques. In this paper the design process which begins with the generation of the Sierpinski Carpet fractal structure by iteratively subdividing a square into smaller squares and removing specific portions following a predefined pattern. The novel approach is based on the Artificial Neural Network (ANN) and a High Frequency Structure Simulator (HFSS) as a computational tool in the designing of proposed antenna. These methods provide accurate simulations of the antenna radiation patterns, impedance characteristics, and resonant frequencies across multiple frequency bands. The optimization process aims to enhance parameters like bandwidth, gain, radiation efficiency, and impedance matching for the frequency band between 2 GHz and 8 GHz, thus providing multiband capabilities of Fractal antenna to be utilized for Wi-Fi & WiMAX systems..The findings of the design and optimization of the Sierpinski Carpet fractal antenna presented in this paper showcase the potential of combining intricate fractal geometries with computational techniques. In this work, Artificial Neural Networks prove to be the optimal choice for antenna design and optimization .This synergy leads to antennas with enhanced performance characteristics, making them valuable components in the ever-evolving field of wireless communication systems.

show abstract

Section: Antenna Design Using Neural Network Modelmentioning

confidence: 99%