Active parametric identification of Gaussian discrete systems based on the time domain linearization and optimal control

Some theoretical and applied aspects of active parametric identification of Gaussian nonlinear discrete systems based on the time domain linearization and optimal control have been considered for the first time. Original results are obtained for the case when parameters of mathematical models to be estimated appear in the state and control equations, as well as in the initial condition and covariance matrices of the dynamic noise and measurement errors. The example of optimal parameter estimation for one model structure is given.