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Optimizing inset-fed rectangular micro strip patch antenna by improved particle swarm optimization and simulated annealing.
Kumar, Jakkuluri Vijaya; Shaby, S Maflin.
Afiliação
  • Kumar JV; School of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
  • Shaby SM; School of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
Network ; : 1-31, 2024 May 28.
Article em En | MEDLINE | ID: mdl-38804502
ABSTRACT
The recent wireless communication systems require high gain, lightweight, low profile, and simple antenna structures to ensure high efficiency and reliability. The existing microstrip patch antenna (MPA) design approaches attain low gain and high return loss. To solve this issue, the geometric dimensions of the antenna should be optimized. The improved Particle Swarm Optimization (PSO) algorithm which is the combination of PSO and simulated annealing (SA) approach (PSO-SA) is employed in this paper to optimize the width and length of the inset-fed rectangular microstrip patch antennas for Ku-band and C-band applications. The inputs to the proposed algorithm such as substrate height, dielectric constant, and resonant frequency and outputs are optimized for width and height. The return loss and gain of the antenna are considered for the fitness function. To calculate the fitness value, the Feedforward Neural Network (FNN) is employed in the PSO-SA approach. The design and optimization of the proposed MPA are implemented in MATLAB software. The performance of the optimally designed antenna with the proposed approach is evaluated in terms of the radiation pattern, return loss, Voltage Standing Wave Ratio (VSWR), gain, computation time, directivity, and convergence speed.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Network Assunto da revista: NEUROLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Índia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Network Assunto da revista: NEUROLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Índia