By Topic

An artificial neural model of the microstrip lines

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Turker, N. ; Yildiz Teknik Universitesi, Istanbul, Turkey ; Gunes, F.

The black-box model of a microstrip transmission line is worked out. The input free variables taken are the substrate thickness, H, the conductor strip width, W, and the conductor thickness, T, as the geometric dimensions of the system, as well as the normalized frequency, fH, (GHz mm), and the dielectric constants, εx, εy; the functions of characteristic impedance, Z0, and effective dielectric constant, εeff, are the results at the output of the black-box. A simple neural network with a single hidden layer is employed for the evaluation inside the black-box, which is activated by a sigmoid function and trained by the Levenberg-Marquard algorithm. Approximate analytic solutions, with empirical adjustment of their numerical constants to achieve the desired accuracy, are utilized to obtain the training and test data. Commonly used materials, such as alumina, PTFE/microfiber glass, gallium arsenide, and RT/Duroid 6006 are applied to the neural network and their Z0 and εeff are obtained as functions of H, W/H, T, fH, εx and εy. This neural network model can be used for the analysis and the synthesis of microstrip circuits, including monolithic microwave integrated circuits.

Published in:

Signal Processing and Communications Applications Conference, 2004. Proceedings of the IEEE 12th

Date of Conference:

28-30 April 2004