Efficient linear macromodeling via least-squares response approximation

We present a least-squares (LS) algorithm for rational function macromodeling of port-to-port responses with discrete-time sampled data. The core routine involves over-determined equations and filtering operation, and avoids numerical-sensitive calculation and initial pole assignment. We demonstrate the fast computation and excellent accuracy and robustness, even with noisy data, in stable response approximation.