Stochastic algorithms in system identification
Harald Höge · International Journal of Control · 1973
In order to identify a linear system with a discrete non-parametric model a class of algorithms which is closely related to the Kalman filter is considered. With an input signal of appropriate statistics these algorithms are easy to calculate and are characterized by a speed of convergence which is comparable to that of a Kalman filter given an imperfect, a priori knowledge of the error covariance matrix.