Modal current distributions on closely coupled microstrip lines: a comparative study of the SDA basis functions

1990 ◽  
Vol 26 (7) ◽  
pp. 464 ◽  
Author(s):  
C.-K.C. Tzuang ◽  
J.-T. Kuo
Author(s):  
Arun K. Subramaniyan ◽  
Liping Wang ◽  
Don Beeson ◽  
John Nelson ◽  
Richard Berg ◽  
...  

This paper provides a comparative study on accuracy and efficiency of metamodels constructed from large datasets. Two examples inspired by large industrial applications are used to identify the best metamodeling technique. Artificial Neural Networks, Radial Basis Functions, Gaussian Process and Nonlinear regression are used to build metamodels. The examples used showcase a broad range of industrial applications in aircraft engines and gas turbines. Although Radial Basis Functions and Gaussian Process models are robust for small data sets, their high computational cost for large datasets reduces their practical application. ANN models are found to perform optimally when large number of training points are readily available and the accuracy requirements are high.


2019 ◽  
Vol 8 (1) ◽  
pp. 51-58
Author(s):  
M. Bayjja ◽  
M. Moubadir ◽  
G. Alsharahi ◽  
M. Aghoutane ◽  
N. Amar Touhami

In this paper, we apply a several wavelets basis functions to the method of moments to modeling the parallel-coupled microstrip lines. The first set of equations is for the shielded microstrip line solved with moment’s method and wavelets. The Green’s function is obtained from the theory of images. The second set are for the parallel-coupled microstrip lines operating in the TEM mode or when the analysis can be based on quasi-static approximation, the properties of coupled lines can be determined from the self- and mutual inductances and capacitances for the lines. To demonstrate the effectiveness and accuracy of the proposed technique, numerical results of even- and odd-mode characteristic impedances, coupling coefficient, percentage sparsity achieved in the impedance matrix, the CPU Time to reverse impedance matrix, and error relative for Daubechies, Coiflets,   Biorthogonal and Symlets wavelets are presented. Numerical results are in good agreement with those in previous publications.


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