Further results on recursive polyhedral description of parameter uncertainty in the bounded-error context

When the (prediction) error is only known to be bounded, it is interesting to characterize the set of all values of the parameters to be estimated that are consistent with the data, error bounds, and model structure. When the error is affine in the parameters, this set is a convex polyhedron which can be fully characterized by enumerating its vertices and supporting hyperplanes. The contribution of this work is threefold. First, a new algorithm for an exact recursive description of the polyhedron is described. Second, a new method for determining the intersection of several polyhedrons (obtained, for example, from different data sets) is proposed. Third, the polyhedral-description approach is extended to output-error models. The procedure is illustrated by an example.<
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