ON OPTIMAL CHOICE OF SAMPLING STRATEGIES FOR LINEAR SYSTEM IDENTIFICATION.

Abstract

The effect of the sampling strategy on the achievable accuracy in linear system identification experiments is presented and the effect of having different sampling rates in each of the subexperiments is investigated. It is shown that the optimal information return can be achieved by use of a finite number of subexperiments. The theory is developed in two parts: the first relating to the identification of a stochastic process from output observations; the second relating to the identification of an input-output transfer function from noisy observations. Examples show that the appropriate choice of sampling strategy can be of paramount importance in identification experiments. The design can be extended in a straightforward manner to safeguard against a single noninformative experiment in the case of diffuse prior distribution for the parameters.