LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
Efficient Linear Macromodeling via Discrete-Time Time-Domain Vector Fitting
VLSID '08 Proceedings of the 21st International Conference on VLSI Design
Least-squares approximation of FIR by IIR digital filters
IEEE Transactions on Signal Processing
IIR Approximation of FIR Filters Via Discrete-Time Vector Fitting
IEEE Transactions on Signal Processing
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We develop a rational function macromodeling algorithm named VISA (Versatile Impulse Structure Approximation) for macromodeling of system responses with (discrete) time-sampled data. The ideas of Walsh theorem and complementary signal are introduced to convert the macromodeling problem into a non-pole-based Steiglitz-McBride (SM) iteration (a class of first- and second-order interpolations) without initial guess and eigenvalue computation. We demonstrate the fast convergence and the versatile macromodeling requirement adoption through a P-norm approximation expansion, using examples from practical data.