Information technology of active parametric identification of stochastic quasi-linear discrete systems

Some theoretical and applied aspects of the active parametric identification of the Gaussian nonlinear discrete systems are considered for the first time. The original results are obtained for the case when the parameters of the mathematical models to be estimated appear in the state and observation equations; the initial conditions and covariance matrices of the dynamic noise and measurements errors were considered. An example of optimal parameter estimation of one model structure is shown.