Set-Membership Identification of Non-Linear Conceptual Models

Identification of conceptual models nonlinear in the parameters from bounded-error data is considered. The assumption that errors are point-wise bounded implies that a set of parameter vectors is found instead of an ‘optimal’ parameter estimate. For our class of models, the Monte Carlo Set-Membership algorithm is appropriate to approximate the exact solution set by a number of feasible realizations. In addition to the feasible parameter set, representing the parametric uncertainty, information about the modelling uncertainty is also provided. In order to obtain realistic predictions both uncertainty sources must be quantified from the available data and evaluated over the prediction horizon. Three ‘real-world’ examples will illustrate the features of this set-membership approach to system identification and prediction.