A new multi-valued neural network for the extraction of lumped models of analog circuits

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 multi-valued neuron neural network with a modified layer and learning process, whose convergence allows the validation of the approximated lumped model. The modified layer is generated by symbolic analysis of the model under exam. The inputs of the neural network are geometrical parameters, while the outputs represent the estimation of the lumped circuit parameters.