Application of the reduction technique becomes possible, since the optimal filtering equations solving the H2 (mean-square) filtering problems have been obtained for linear systems with measurement delay.
In contrast to the results previously obtained for linear time delay systems, the paper reduces the original H∞ filtering problems to H2 (optimal mean-square) filtering problems, using the technique proposed in [1].
In contrast to the results previously obtained for linear time delay systems, the paper reduces the original H∞ filtering problem to an H2 (optimal mean-square) filtering problem, using the technique proposed in [1].
The PSC maps were interpolated to volumes with 1-mm3 voxels, co-registered, converted to Talairach coordinate-space, and blurred using a 4-mm Gaussian root mean square filter.
In this paper, we address this issue through a study and an empirical evaluation of a set of online algorithms for regression, which includes the baseline Hoeffding-based regression trees, online option trees, and an online least mean squares filter.
Simulation results demonstrate the superiority of the proposed algorithm over existing algorithms such as the filtered-x least mean square, filtered-x logarithmic least mean square, filtered-x normalized least mean square and filtered weight filtered-x normalized least mean square algorithms in terms of convergence rate and noise reduction.
This paper investigates the mean-square filtering problem for a linear time delay systems with Gaussian white noises.
The original problem is reduced to the mean-square filtering problem for incompletely measured bilinear time-delay system states over linear observations.
The obtained mean-square filter for the extended state vector also serves as the mean-square identifier for the unknown parameters.
It is demonstrated that the estimates produced by the designed sliding-mode mean-square filter and the Kalman Bucy filter yield the same estimation-error variance.
