A comparative study of neural networks for input resistance computation of electrically thin and thick rectangular microstrip antennas

Güney, Kerïm; Gültekin, Sinan

doi:10.1134/s1064226907050014

Cited by 5 publications

(6 citation statements)

References 51 publications

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“…However, in [51,52], more outputs were calculated by using multiple ANFIS. In general, in the literature, each different parameter of each different MSA was computed by using a different individual ANN [53][54][55] or ANFIS model [56][57][58][59][60]. Single neural models were proposed in [61,62] for simultaneously calculating the resonant frequencies of the rectangular, circular, and triangular MSAs.…”

Section: Introductionmentioning

confidence: 99%

Concurrent Neuro-Fuzzy Systems for Resonant Frequency Computation of Rectangular, Circular, and Triangular Microstrip Antennas

Güney¹,

Sarıkaya²

2008

PIER

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Abstract-A method based on concurrent neuro-fuzzy system (CNFS) is presented to calculate simultaneously the resonant frequencies of the rectangular, circular, and triangular microstrip antennas (MSAs).The CNFS comprises an artificial neural network (ANN) and an adaptive-network-based fuzzy inference system (ANFIS). In a CNFS, neural network assists the fuzzy system continuously (or vice versa) to compute the resonant frequency. The resonant frequency results of CNFS for the rectangular, circular, and triangular MSAs are in very good agreement with the experimental results available in the literature.

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Section: Introductionmentioning

confidence: 99%

Concurrent Neuro-Fuzzy Systems for Resonant Frequency Computation of Rectangular, Circular, and Triangular Microstrip Antennas

Güney¹,

Sarıkaya²

2008

PIER

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“…Neurons in the layer of entry only act as buffers for distributing the input signals x i to neurons in the hidden layer. Each neuron in the hidden layer sums its input signals x i after weighting them with the strengths of the respective connections w ji from the layer of entry and computes its output y j as a function f of the sum, namely where f can be a simple threshold function or a sigmoid or hyperbolic tangent function [23]. The output of neurons in the output layer is computed likewise.…”

Section: Annsmentioning

confidence: 99%

Neurospectral computation for the resonant characteristics of microstrip patch antenna printed on uniaxially anisotropic substrates

Barkat

Bedra

Fortaki

et al. 2016

Int. J. Microw. Wireless Technol.

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Modeling and design of rectangular microstrip patch printed on isotropic or anisotropic substrate are accomplished in this paper. The use of spectral domain method in conjunction with artificial neural networks (ANNs) to compute the resonant characteristics of rectangular microstrip patch printed on isotropic or anisotropic substrates. The moment method implemented in the spectral domain offers good accurateness, but its computational cost is high owing to the evaluation of the slowly decaying integrals and the iterative nature of the solution process. The paper introduces the electromagnetic knowledge combined with ANN in the analysis of rectangular microstrip antenna on uniaxially anisotropic substrate to reduce the complexity of the spectral domain method and to minimize the CPU time necessary to obtain the numerical results. The numerical comparison between neurospectral and conventional moment methods shows significant improvements in time convergence and computational cost. Hence, the use of neurospectral approach presented here as a promising fast technique in the design of microstrip antennas.

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“…Multilayer perceptrons (MLP) have been applied successfully to solve some difficult and diverse problems by training them in a supervised manner with a highly popular algorithm known as the error back propagation algorithm [21]. Where f can be a simple threshold function or a sigmoid or hyperbolic tangent function [22]. The output of neurons in the output layer is computed similarly.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Design of rectangular microstrip antenna with rectangular aperture in the ground plane using artificial neural networks

Benkouda

Fortaki

Bedra

et al. 2015

The 7th International Conference on Information Technology

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In this paper, we propose a general design of rectangular microstrip antenna with and without rectangular aperture over ground plane, based on artificial neural networks (ANN) in conjunction with spectral domain formulation. In the design procedure, syntheses ANN model is used as feed forward network to determine the resonant frequency and bandwidth. Analysis ANN model is used as the reversed of the problem to calculate the antenna and aperture dimensions for the given resonant frequency, dielectric constant and height of substrate. The spectral domain formulation combined with artificial neural network in the analysis and the design of rectangular antenna to reduce the complexity of the spectral approach and to minimize the CPU time necessary to obtain the numerical results. The results obtained from the neural models are in very good agreement with the experimental results available in the literature.

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A comparative study of neural networks for input resistance computation of electrically thin and thick rectangular microstrip antennas

Cited by 5 publications

References 51 publications

Concurrent Neuro-Fuzzy Systems for Resonant Frequency Computation of Rectangular, Circular, and Triangular Microstrip Antennas

Concurrent Neuro-Fuzzy Systems for Resonant Frequency Computation of Rectangular, Circular, and Triangular Microstrip Antennas

Neurospectral computation for the resonant characteristics of microstrip patch antenna printed on uniaxially anisotropic substrates

Design of rectangular microstrip antenna with rectangular aperture in the ground plane using artificial neural networks

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