Subspace Methods for System Identification

System identification provides methods for the sensible approximation of real systems using a model set based on experimental input and output data. Tohru Katayama sets out an in-depth introduction to subspace methods for system identification in discrete-time linear systems thoroughly augmented with advanced and novel results. The text is structured into three parts. First, the mathematical preliminaries are dealt with: numerical linear algebra; system theory; stochastic processes; and Kalman filtering.