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The main contribution of this paper is a recursive algorithm for parametric system identification in the presence of both noise and model uncertainties. The estimates provided by this algorithm are not invalidated, after a learning period, by the observed input-output data and the assumed system and uncertainty structures. A complementary off-line algorithm derived from the on-line algorithm is...
This paper deals with some issues involving a parameter estimation approach that yields estimates consistent with the data and the given a priori information. The first part of the paper deals with the relationships between various noise models and the size of the resulting membership set, the set of parameter estimates consistent with the data and the a priori information. When there is some...
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