Adaptive algorithms for non-stastical parameter estimation in linear models

A non-statistical approach to parameter estimation is presented. Assuming bounded noise, two algorithms are developed to obtain membership sets in the parameter space which are consistent with the set of measurements. The set theoretic approach enables the evaluation of information pertaining to the estimation problem as a new measurement is obtained. The proposed algorithms are shown to converge at least as fast as the least squares procedure. This performance is obtained while only about 10% of the data is actually used in the identification. The proposed algorithms are thus very attractive in the context of speach analysis where the assumption of bounded noise is easy to justify.