Design of Microstrip Antennas Using Artificial Neural Networks

This paper gives the construction and parametric analysis of low loss RF MEMS switch at low DC frequencies up to 8GHz which support reconfigurable antenna devices. The switch used here exhibit excellent RF characteristics in terms of power and current consumption with typical pull-in voltages lies between 1.912 to 2.125V. The switch also exhibit low insertion loss in the range of (0.01 to 0.03dB at DC-3.0GHz and 0.03 to 0.07dB at 3.0-8GHz) in on condition and with reasonable isolation (9dB at DC-3.0GHz and 9dB to 17dB at 3.0-8.0GHz) at off position. Further the return loss in up and down states are calculated as -40 to -32dB and less than -0.16dB at DC-8GHz respectively. A feed-forward back-propagation (FFBP) algorithm of neural network is also presented for the validation of changing the anchor arm length, width of upper beam and dielectric of RF MEMS switch. The algorithm assembles new data samples during training from finite element method (FEM) based electromagnetic simulation tool HFSS. Results of the ANN are compared with those of the electromagnetic solver. These physical dimensions are varied and design is optimizing for low power consumption and to achieve acceptable level of s parameters. The developed algorithm allows the optimisation solutions of the design to be carried out by replacing repeated simulations and also provides lesser processing times whilst still retaining an excellent accuracy as compared with finite element modelling. The optimized isolation and insertion loss results are in very good agreement up to 99.5% as compare with the theoretical results.

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“…It has wide potential with multiband support which is to be sight for different aerospace and telecommunication applications. [6,[7][8][9].…”

Section: Rajesh Khannamentioning

confidence: 99%

A novel design and optimization approach of RF MEMS switch for reconfigurable antenna using ANN method

Chawla

Khanna

2012

2012 International Conference on Communications, Devices and Intelligent Systems (CODIS)

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“…Neural networks can be used for the applications of wireless communications. In area of microwave applications, ANNs have been used to design Rectangular Microstrip Patch Antenna (RMPA) [10][11][12][13] and CMPAs [14,15]. These can also be used to calculate different parameters such as feed position [16], resonant resistance [17], input impedance [18], radiation efficiency [19], resonant frequencies of triangular and RMPAs [20].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Design of Circular Fractal Antenna Using Artificial Neural Networks

Sivia¹,

Pharwaha²,

Kamal³

2013

PIER B

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A Neural Network is a simplified mathematical model based on Biological Neural Network, which can be considered as an extension of conventional data processing technique. In this paper, an Artificial Neural Network (ANN) based simple approach is proposed as forward side for the design of a Circular Fractal Antenna (CFA) and analysis as reverse side of problem. Proposed antenna is simulated up to 2nd iteration using method of moment based IE3D software. Antenna is fabricated on Roger RT 5880 Duroid substrate (High frequency material) for validation of simulated, measured and ANN results. The main advantage of using ANN is that a properly trained neural network completely bypasses the complex iterative process for the design and analysis of this antenna. Results obtained by using artificial neural networks are in accordance with the simulated and measured results.

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“…The calculation of exact patch dimensions of rectangular microstrip patch antenna becomes extremely important where the antenna size is drastically small. A number of papers have been appeared on the calculation of patch dimension of microstrip antennas [1][2][3]. However, these papers suffer considerable deviation in the calculated value of patch dimensions compared to theoretical and simulation findings.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in the Estimation of Patch Dimensions of Rectangular Microstrip Antennas

Thakare¹,

Singhal²

2011

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Artificial Neural Network (ANNs) techniques are recently indicating a lot of promises in the application of various micro-engineering fields. Such a use of ANNs for estimating the patch dimensions of a microstrip line feed rectangular microstrip patch antennas has been presented in this paper. An ANN model has been developed and tested for rectangular patch antenna design. The performance of the neural network has been compared with the simulated values obtained from IE3D EM Simulator. It transforms the data containing the dielectric constant (ε r ), thickness of the substrate (h), and antenna's dominant-mode resonant frequency (f r ) to the patch dimensions i.e. length (L) and width (W) of the patch. The different variants of back propagation training algorithm of MLFFBP-ANN (Multilayer feed forward back propagation Artificial Neural Network) and RBF-ANN (Radial basis function Artificial Neural Network) has been used to implement the network model. The results obtained from artificial neural network when compared with simulation results, found satisfactory and also it is concluded that RBF network is more accurate and fast as compared to different variants of back propagation training algorithms of MLPFFBP. The ANNs results are more in agreement with the simulation findings. Neural network based estimation has the usual advantage of very fast and simultaneous response of all the outputs.

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Design of Microstrip Antennas Using Artificial Neural Networks

Cited by 26 publications

References 4 publications

A novel design and optimization approach of RF MEMS switch for reconfigurable antenna using ANN method

A novel design and optimization approach of RF MEMS switch for reconfigurable antenna using ANN method

Analysis and Design of Circular Fractal Antenna Using Artificial Neural Networks

Artificial Intelligence in the Estimation of Patch Dimensions of Rectangular Microstrip Antennas

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