A novel optimization technique for the parameter identification of microwave monolithic integrated circuit is presented. It is based on a hybrid neural network whose learning process convergence allows the validation of the circuit approximated lumped model. The main feature of such a learning process is that no external desired signal is required and the neural network can be considered of the unsupervised type. Furthermore the neural network output represents the lumped circuit parameter estimation.
A Neural Architecture for the Parameter Extraction of High Frequency Devices / Avitabile, Gianfranco; Chellini, B.; Fedi, G.; Luchetta, A.; Manetti, S.. - (2001), pp. 577-580. (Intervento presentato al convegno IEEE International Symposium on Circuits and Systems, ISCAS 2001. tenutosi a Sydney , Australia nel May 6-9, 2001) [10.1109/ISCAS.2001.921376].
A Neural Architecture for the Parameter Extraction of High Frequency Devices
AVITABILE, Gianfranco;
2001-01-01
Abstract
A novel optimization technique for the parameter identification of microwave monolithic integrated circuit is presented. It is based on a hybrid neural network whose learning process convergence allows the validation of the circuit approximated lumped model. The main feature of such a learning process is that no external desired signal is required and the neural network can be considered of the unsupervised type. Furthermore the neural network output represents the lumped circuit parameter estimation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
