In this paper a new formulation of the problem of identification of discrete time linear models in the case of bounded errors is proposed. The bounds of the error at each sampling time are specified over a measurement noise rather than over an equation error. The method provides parameter...

A nonlinear Bounded-Error Estimation method is applied to the case of ellipsometric measurement of film properties. It is shown that this method can be used with advantage for estimating the magnitude of the measurement errors including systematic errors.

Structural identifiability is concerned with whether the parameters of a specified model can be identified (uniquely or locally) from a given input-output experiment, assuming perfect data, while regional indistinguishability is concerned with generating all models, within a given class, which...

In this paper an expert system for 2-D and 3-D object recognition combining numeric and symbolic representations is presented. Starting with some basic concepts, a new method for the boundary representation on 2-D objects is given, and then a method for 3-D object recognition is provided which...

The Optimal Bounding Ellipsoid (OBE) algorithms are viable alternatives to conventional adaptive filtering algorithms in situations where the noise does not satisfy the usual stationarity and whiteness assumptions. An example is shown in which the performance of an OBE algorithm is seen to be...

Set-membership estimation (or parameter bounding) uses a non-statistical description of the acceptable error between the measurements and corresponding model outputs in the form of prior upper and lower bounds. It aims at characterizing the set S of all parameter vectors that are consistent with...