In this paper,a least-mean-squares(lms) finitememory(FM) estimator for a stochastic discrete-timestatespace model is obtained by taking the conditional expectation of the estimated state given a finite number of in...
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In this paper,a least-mean-squares(lms) finitememory(FM) estimator for a stochastic discrete-timestatespace model is obtained by taking the conditional expectation of the estimated state given a finite number of inputs and outputs measured on the recent finite *** a priori state information is not involved and any arbitrary constraints are not *** a general discrete-timestatespace model with both system and measurement noises,the lms FM estimator is represented in a *** turns out that the proposed lms FM estimator has the unbiased property and the linear structure with respect to inputs and outputs on the recent finite horizon.
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