A Matlab TOOLBox for exact linear time-invariant system identification is presented. The emphasis is on the variety of possible ways to implement the mappings from data to parameters of the data generating system. The considered system representations are input/state/output, difference equation, and left matrix fraction.
KEYWORDS: subspace identification, deterministic subspace identification, balanced model reduction, approximate system identification, MPUM.
The TOOLBox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the TOOLBox: transformation functions, misfit computation functions, and approximation functions. The approximation functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model representation from another.
KEYWORDS: Total least squares, generalized total least squares, software implementation.
