On tuning passive black-box macromodels of LTI systems via adaptive weighting

This paper discusses various approaches for tuning the accuracy of rational macromodels obtained via black-box identification or approximation of sampled frequency responses of some unknown Linear and Time-Invariant system. Main emphasis is on embedding into the model extraction process some information on the nominal terminations that will be connected to the model during normal operation, so that the corresponding accuracy is optimized. This goal is achieved through an optimization based on a suitably defined cost function, which embeds frequency-dependent weights that are adaptively refined during the model construction. A similar procedure is applied in a postprocessing step for enforcing model passivity. The advantages of proposed algorithm are illustrated on a few application examples related to power distribution networks in electronic systems.