Artificial Neural Networks for Calculating the Characteristic Impedance of Air-Suspended Trapezoidal and Rectangular-Shaped Microshield Lines

Güney, Kerïm; Yildiz, Celal; Kaya, Sabri; Türkmen, Mustafa

doi:10.1163/156939306777442917

Cited by 27 publications

(19 citation statements)

References 18 publications

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“…We selected the training, validation and test data from overall measurement data randomly to minimize the overall fitting error. R 2 value of a model is the proportion of the total variability in the dependent variable that is accounted for by the model [30]. R 2 (R 2 = 1 for best matching) value of the ANN model implies that the constructed neural network model makes very close estimates for the observed values.…”

Section: Water-to-cement Ratio Estimation Using Annsmentioning

confidence: 99%

Water-to-Cement Ratio Prediction Using Anns From Non-Destructive and Contactless Microwave Measurements

Hasar¹,

Akkaya²,

Aktan³

et al. 2009

PIER

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Abstract-In concrete industry, there is a need for water-to-cement ratio (w/c) estimation of cement-based materials since the w/c ratio of cement mixtures is typically given at the batch plant, and this ratio, sometimes, is deliberately changed to have a more workable cement mixture. To meet the requirements of accurate w/c ratio determination of cement-based materials, in this research paper, we propose an artificial neural network approach for w/c ratio estimation of these materials using free-space non-contact reflection and transmission measurements of mortar specimens with w/c ratios of 0.40, 0.45, 0.50, 0.55 and 0.60. We have tested the network and observed less than 5 percent difference between the estimated and known values of w/c = 0.50.

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Section: Water-to-cement Ratio Estimation Using Annsmentioning

confidence: 99%

Water-to-Cement Ratio Prediction Using Anns From Non-Destructive and Contactless Microwave Measurements

Hasar¹,

Akkaya²,

Aktan³

et al. 2009

PIER

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show abstract

“…To overcome this problem, we propose a NN-based model for the S-parameters estimation. Although NN is used for solving some various antenna problems [30][31][32][33], NN using for the calculation of S-parameters is proposed for the first time.…”

Section: Dipole Array Design For Bearable Coupling Between Adjacent Ementioning

confidence: 99%

Nonuniformly Spaced Linear Array Design for the Specified Beamwidth/Sidelobe Level or Specified Directivity/Sidelobe Level With Coupling Consideration

Oraizi¹,

Fallahpour²

2008

PIER M

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Abstract-In this paper, we investigate nonuniformly spaced linear arrays (NUSLA) rigorously. Several important problems in NUSLA design are solved with the combination of the Genetic Algorithm and Conjugate Gradient method (GA-CG). The pattern synthesis for the specified beamwidth and minimum achievable sidelobe level (SLL) are performed and for the first time, the graphs which show the relation between the beamwidth, sidelobe level and number of elements for NUSLA are derived. Also, the NUSLA's pattern for the specified directivity and sidelobe level is synthesized. The graphs showing the behavior of NUSLA relative to the increase of its length for a fixed number of elements are derived. These graphs show the relations between the directivity and sidelobe level of NUSLA with its length. As a practical design, an array of parallel dipoles is designed for specified beamwidth/sidelobe level or specified directivity/sidelobe level. Furthermore, a novel Neural Network based model for the NUSLA is presented for the rapid and accurate computation of Sparameters. The computed S-parameters are used for the computation of coupling among elements. Then the GA-CG method can adjust these values in the synthesis process to achieve desired pattern and bearable coupling among elements.

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“…ANNs have been applied in many areas because of these features. Accurate and efficient microwave components, filters and microstrip antennas have been analyzed and designed with the use of ANNs [15][16][17][18][19][20][21][22][23][24][25]. In these applications, ANNs have more general functional forms and are usually better than the classical techniques, and provide simplicity in real-time operation.…”

Section: Introductionmentioning

confidence: 99%

Quasi-Static Models Based on Artificial Neural Neworks for Calculating the Characteristic Parameters of Multilayer Cylindrical Coplanar Waveguide and Strip Line

Yildiz¹,

Türkmen²

2008

PIER B

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Abstract-In this paper, two different neural models are proposed for calculating the quasi-static parameters of multilayer cylindrical coplanar waveguides and strip lines. These models were basically developed by training the artificial neural networks with the numerical results of quasi-static analysis. Neural models were trained with four different learning algorithms to obtain better performance and faster convergence with simpler structure. When the performances of neural models are compared with each other, the best test results are obtained from the multilayered perceptrons trained by the LevenbergMarquardt algorithm. The results obtained from the neural models are in very good agreements with the theoretical results available in the literature.

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Artificial Neural Networks for Calculating the Characteristic Impedance of Air-Suspended Trapezoidal and Rectangular-Shaped Microshield Lines

Cited by 27 publications

References 18 publications

Water-to-Cement Ratio Prediction Using Anns From Non-Destructive and Contactless Microwave Measurements

Water-to-Cement Ratio Prediction Using Anns From Non-Destructive and Contactless Microwave Measurements

Nonuniformly Spaced Linear Array Design for the Specified Beamwidth/Sidelobe Level or Specified Directivity/Sidelobe Level With Coupling Consideration

Quasi-Static Models Based on Artificial Neural Neworks for Calculating the Characteristic Parameters of Multilayer Cylindrical Coplanar Waveguide and Strip Line

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