Microstrip antenna design using artificial neural networks

Abstract

Neural-network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behavior of passive/active components/circuits. This work describes the fundamental concepts in this emerging area aimed at teaching RF/microwave engineers what neural networks are, why they are useful, when they can be used, and how to use them to model microstrip patch antenna. This work studies in-depth different designs and analysis methods of microstrip patch antenna using artificial neural-network and different network structure are also described from the RF/microwave designer's perspective. This article also illustrates two examples of microstrip antenna design and validating the utility of ANN in the area of microstrip antenna design.

Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks

“…Hence, the individual neural models [9][10][11][12][13][14][15][16] and generalized neural approaches [17][18][19][20] have been used only for resonance frequency and/or geometric dimensions of microstrip patch antennas. Few neural networks models [21][22][23] have also been proposed for designing the slotted microstrip antennas. But unfortunately, simultaneous computations of different performance parameters (i.e., resonance frequency, gain, directivity, antenna efficiency, and radiation efficiency) using neural networks model have been rarely attempted in the available literature [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] as these parameters are essentially required for antenna designers for synthesizing the MSAs.…”

“…Few neural networks models [21][22][23] have also been proposed for designing the slotted microstrip antennas. But unfortunately, simultaneous computations of different performance parameters (i.e., resonance frequency, gain, directivity, antenna efficiency, and radiation efficiency) using neural networks model have been rarely attempted in the available literature [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] as these parameters are essentially required for antenna designers for synthesizing the MSAs. In the proposed work, the authors have extended their earlier works of generalized neural networks modeling [24][25][26][27][28][29][30] for predicting different performance parameters (i.e., resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies) of slotted microstrip antennas for dualfrequency operation.…”

Investigations for Performance Improvement of X-Shaped Rmsa Using Artificial Neural Network by Predicting Slot Size

“…Different ANN models [10][11][12][13][14][15][16][17][18] were used for analyzing and synthesizing of microstrip patch antennas. Few other neural models have been proposed for slot loaded microstrip patch antennas in [19,20]. Robustillo et al [21] have designed a contoured-beam reflectarray for a EuTELSAT European coverage using stacked patch element with the help of ANN.…”

An Application of Artificial Neural Network to Compute the Resonant Frequency of E–Shaped Compact Microstrip Antennas

“…There exist several approaches which vary in accuracy and computational efforts have been proposed to analyze and design microstrip antennas. The most widely used can be listed as formulation methods [1,5,24] and artificial intelligent systems [4,8,15,17,20,23]. Formulation methods are commonly derived with the aid of the optimization algorithm such as genetic, particle swarm, differential evolution etc.…”

“…Formulation methods are commonly derived with the aid of the optimization algorithm such as genetic, particle swarm, differential evolution etc. The most well-known artificial intelligent systems are the artificial neural network (ANN) [4,8,11,13,15,17,20,23] and the adaptive neuro-fuzzy interference system (ANFIS) [13]. ANN attempts to model nonlinear problems by employing a mathematical model of the structure of the brain.…”