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To give another perspective, our quality-weighted statistics t* can also be considered as an implementation of a filtering method.
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A comparison is performed between the discrete and continuous implementation of Kalman filtering and optimal control for a stationary process.
This is because one can formulate optimal control as a Kalman filter (exactly for linear quadratic control) and predictive coding is a biologically plausible implementation of Bayesian filtering.
This facilitates the implementation of the filtering algorithm in high dimensional settings using a remarkably small number of particles.
The new advances include: (1) stabilization of open-loop unstable plants using a state feedback strategy, (2) incorporation of an adjustable pre-filter to achieve offset-free control, (3) implementation of a Kalman filter for state estimation, and (4) connection of the multiple model approach with non-linear model predictive control to achieve a precise control objective.
The main new features of the current approach include: (1) stabilization of open-loop unstable plants using a state feedback strategy, (2) incorporation of an adjustable pre-filter to achieve offset-free control, and (3) implementation of a Kalman filter for state estimation where necessary.
It is important to consider this in the implementation of inverse filtering algorithms.
We found a Matlab implementation of a Kalman filter that was easy to use on our data.
Presented is an electronic implementation of a matched filter intended for chaos-based communication systems.
As such, it specifies the direct-form I (DF-I) implementation of a digital filter, one of four direct-form structures to choose from.
The latter is possible due to an elevation scan capability and by the implementation of a new filter bank design.
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