Abstract
Abstract The optimal solution for the state estimation problem of an unknown, constant parameter system in a discret parameter space of models, is obtained by the sum of the states estimated weigheted by a posteriori probability of models. When the true model doesn't belong to the space of models, it can be shown that this algorithm converges to only one model in the space of models. This behaviour is justified by the bayesian characteristic of the algorithm. This paper proposes a new algorithm to determine the models' probabilities, in order to obtain, effectively, a multimodel estimation algorithm. Computacional results provided by the proposed algorithm are very close to the optimal solution.
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