Industrial Automatic Control Systems and Controllers Annotation << Back Statistical Identifi cation. Properties of Parameter Estimates and Conditions V.I. Kuznetsov The paper provides a rationale for the properties of the parameter estimates and the states of dynamic systems obtained as a result of a comprehensive analysis of measurement data in an adaptive manner. Consistently considered the transition from probability density estimates for the likelihood function, and then to the Fisher information matrix, which allows to determine the lower limit of the Cramer-Rao. Based on the form of the distribution and the optimality criterion is justifi ed unbiased estimates. Methodical Approaches to the consistency of estimates for a single experiment. A conversion matrix expression defi ning the lower limit of the Cramer-Rao to form corresponding equation for the covariance matrix of the error estimates, and proved the effectiveness of estimates of parameters and conditions. Separately analyzed the effect of extending the model states allowed by the transition to linear maps nonlinear models measurements, the properties of the parameter estimates and the states of dynamic systems. Shows the independence of the estimates obtained on the equations of the model are included in the measurement vector states or not, and on the basis of this conclusion proved the adequacy of estimates of parameters and conditions. Keywords: suffi ciency; nonshifted; estimate; parameter; estimation; parameter; probability density; condition; convergence; likelihood function; effectiveness. Contacts: E-mail: vi_kuznetsov@bk.ru Pp. 40-45.