Convergence of continuous time stochastic ELS parameter estimation (Q1098164)

This paper presents continuous-time adaptive estimation schemes associated with a class of finite-dimensional, time-invariant, linear stochastic signal models. A global convergence theory is given for such schemes under a coloured noise/prefiller positive real condition which may be side-stepped for moving average models. Attention is first focused on extended least squares (ELS) identification of stable signal models driven by bounded inputs. Next, some convergence results for least squares (LS) estimation of unstable signal models are extracted from the earlier theory. The ELS and LS theories suggest construction of identification schemes based on both ELS and LS. (Abridged author's summary.)