FAST SYSTEM IDENTIFICATION AND MODEL REDUCTION SOLVERS

Fast system identification and model reduction solvers

Efficient numerical techniques for multivariable system identification and model reduction are investigated. The techniques are implemented in the system identification and model reduction toolboxes based on the Fortran 77 Subroutine Library in Control Theory (SLICOT). Besides highly performant Fortran drivers and computational routines, these toolboxes provide MATLAB or Scilab interfaces, impl...

Model Reduction and System Identification for Master Equation Control Systems

A master equation describes the continuous-time evolution of a probability distribution, and is characterized by a simple bilinear structure and an often-high dimension. We develop a model reduction approach in which the number of possible configurations and corresponding dimension is reduced, by removing improbable configurations and grouping similar ones. Error bounds for the reduction are de...

Adaptive Nonlinear Model Reduction for Fast Power System Simulation

 Abstract--The paper proposes a new adaptive approach to power system model reduction for fast and accurate time-domain simulation. This new approach is a compromise between linear model reduction for faster simulation and nonlinear model reduction for better accuracy. During the simulation period, the approach adaptively switches among detailed and linearly or nonlinearly reduced models based...

Stability Analysis of Fast Toeplitz Linear System Solvers

We present a numerical stability analysis of the Bareiss and Levinson algorithms for solving a symmetric positive definite Toeplitz system of linear equations. CommentsOnly the Abstract is given here. The full report appeared as [1]. For a revision, see [2]. References[1] A. W. Bojanczyk, R. P. Brent and F. R. de Hoog, Stability Analysis of Fast Toeplitz Linear System Solvers,Report...

Parallel Nonnegative Least Squares Solvers for Model Order Reduction