Identification of a discrete-time dynamical system

A novel generalized minimum variance (GMV) system identification algorithm is developed, and its performance is gauged against the established general ized least squares (GLS) estimation algorithm. The emphasis of the proposed GMV algorithm is on the rigorous treatment of measurement noise for dynami cal system identification. A careful analysis of the measurement situation on hand yields a novel fixed-point calculation-based parameter estimation a lgorithm. The novel and established algorithms are compared in carefully pe rformed and reproducible experiments which include measurement noise. Diffe rences are apparent under small (measurement) sample operation, whereas und er sufficient excitation, the algorithms produce statistically similar resu lts.