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Secondly, an optimization model for state estimator design with minimum estimation error in time domain and PSD constraint in frequency domain is established.
Future work will concentrate on the advanced estimator design with respect to covariance matrix estimation.
One of the most frequently used ways for a CFO estimator design is to adopt maximum-likelihood (ML) estimation, achieving high accuracy tightly close to the Cramer-Rao lower bounds (CRLBs).
Different from the widely-studied full-order state estimator design, this paper focuses on dealing with the reduced-order state estimation problem for delayed recurrent neural networks.
Two estimator design approaches are proposed.
Hence, a successful estimator design is possible.
These correlations are taken into account in the estimator design.
An important issue for the estimator design is the numerical conditioning of the equation system.
A method of robust, nonlinear, multi-rate, state estimator design is presented.
Finally, simulation results are provided to illustrate the effectiveness of the proposed estimator design approach.
A common matrix inequality formulation is used in characterization of estimator design equations.
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