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.
A Neural Network Approach to Predict the Crosstalk in Non-Uniform Multiconductor Transmission Lines / Cannas, B.; Fanni, A.; Maradei, Francescaromana. - STAMPA. - 1:(2002), pp. 573-576. (Intervento presentato al convegno IEEE International Symposium on Circuits and Systems 2002 tenutosi a Phoenix, AZ, USA nel May 26-29, 2002) [10.1109/ISCAS.2002.1009905].
A Neural Network Approach to Predict the Crosstalk in Non-Uniform Multiconductor Transmission Lines
MARADEI, Francescaromana
2002
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
