Experiment design for bounded-error models

The problem of experiment design for parameter bounding is addressed. The measurement errors are assumed to be bounded, with no other hypothesis on their distribution. An experiment is defined as optimal when it minimizes the volume of the set of the parameters that are consistent with the data, model structure, and error bounds. Linear and nonlinear model structures are considered, with special attention to situations where the set of consistent parameters may be disconnected.