Abstract
A novel identification technique for lumped models of general distributed circuits (i.e. microwave transmission lines, monolithic integrated circuits and filters) is presented. The approach is based on a hybrid neural network having based on Multi-valued neurons network with a modified layer and learning process, whose convergence allows the validation of the circuit approximated lumped model. The modified layer is generated by symbolic analysis of the model under exam. The inputs of the network are geometrical parameters and the neural network output represents the lumped circuit parameter estimation.
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