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Bandwidth Analysis of Dual-Feed Slotted Antenna Using Artificial Neural Networks
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Author(s): Archana Lala (SR Group of Institutions, India), Kunal Lala (Raj Kumar Goel Institute of Technology, India)and Vinod Kumar Singh (SR Group of Institutions, India)
Copyright: 2021
Pages: 15
Source title:
Emerging Materials and Advanced Designs for Wearable Antennas
Source Author(s)/Editor(s): Vinod Kumar Singh (SR Group of Institutions, India), Vikas Dubey (Bhilai Institute of Technology, India), Anurag Saxena (SR Group of Institutions, India), Ratnesh Tiwari (Bhilai Institute of Technology, India)and Himani Goyal Sharma (Department of Electrical Engineering, Poornima College of Engineering, Jaipur, India)
DOI: 10.4018/978-1-7998-7611-3.ch006
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Abstract
In this chapter, artificial neural network is used for the estimation of bandwidth of a dual feed microstrip antenna. The MLPFFBP-ANN and RBF-ANN are used to implement the neural network model. The simulated values for training and testing the neural network are obtained by simulating the antenna on IE3D software. The results obtained by using ANNs and IE3D simulation are compared and are found quite acceptable, and also it is concluded that RBF network is more accurate and fast as compared to back propagation algorithm of MLPFFBP. The anticipated is applicable to operate in triple band from 2.208GHz-5.35GHz, 2.358GHz-2.736GHz, and 3.815GHz-5.143GHz. The antenna is also fabricated with FR-4 glass epoxy material. The experimental results, simulated results of IE3D, and simulated results of neural network are compared.
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