Title :
A neural network approach to predict the crosstalk in non-uniform multiconductor transmission lines
Author :
Cannas, Barbara ; Fanni, Alessandra ; Maradei, Francescaromana
fDate :
6/24/1905 12:00:00 AM
Abstract :
A neural network approach is proposed for the prediction of crosstalk in non-uniform multiconductor transmission lines. The non-uniform multiconductor transmission lines is approximated as a cascade of uniform sections and the neural network is suitably trained with few cross section configurations to learn the behavior of the per unit length parameters. The procedure allows a fast and accurate prediction tool to analyze crosstalk in non-uniform multiconductor transmission lines.
Keywords :
crosstalk; electrical engineering computing; electromagnetic compatibility; multiconductor transmission lines; neural nets; transmission line theory; EMC; cross section configurations; crosstalk prediction; electromagnetic compatibility; multiconductor transmission lines; network training; neural network approach; nonuniform transmission lines; uniform sections cascade; Crosstalk; Electromagnetic compatibility; Electronic equipment; Electronics industry; Helium; Intelligent networks; Multiconductor transmission lines; Neural networks; Power cables; Wire;
Conference_Titel :
Circuits and Systems, 2002. ISCAS 2002. IEEE International Symposium on
Print_ISBN :
0-7803-7448-7
DOI :
10.1109/ISCAS.2002.1009905
