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A linear fixed order state estimator for state estimation is presented for various estimation error performance criteria in a unified framework.
Here, a robust estimator for state estimation of a class of noisy nonlinear gene networks with uncertain process noise is presented.
An unscented Kalman filter (UKF) is proposed for state estimation.
41 (2002) 932.] for state estimation purposes only.
Particle filters for state estimation of discrete time dynamic stochastic systems are treated.
This paper explores performance of various methods for state estimation of radar tracking models.
Lyapunov theory is used to guarantee stability for state estimation and neural network weight errors.
In this study, a novel strategy for state estimation of fed-batch fermentation process is proposed.
Particle filters for state estimation of discrete time stochastic systems are treated.
It plays an important role for state estimation and parameter estimation in control theory [1 4].
It directly approximates the covariance matrix and is, in this context, used just for state estimation.
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