Convergence analysis of parametric identification methods

Abstract

A certain class of methods to select suitable models of dynamical stochastic systems from measured input-output data is considered. The methods are based on a comparison between the measured outputs and the outputs of a candidate model. Depending on the set of models that is used, such methods are known under a variety of names, like output-error methods, equation-error methods, maximum-likelihood methods, etc. General results are proved concerning the models that are selected asymptotically as the number of observed data tends to infinity. For these results it is not assumed that the true system necessarily can be exactly represented within the chosen set of models. In the particular case when the model set contains the system, general consistency results are obtained and commented upon. Rather than to seek an exact description of the system, it is usually more realistic to be content with a suitable approximation of the true system with reasonable complexity properties. Here, the consequences of such a viewpoint are discussed.