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In the above case, if the available information regarding the dynamics are incomplete or imperfectly known, then the prediction using the state model will be inaccurate.
The total number of pixels is given as N=25×25=625, each of size 1 km 2. Using this, we generate the states u 2…u 8 using the state model mentioned in (6).
Also, for a highly underdetermined (M≪N) and unpredictable measurement matrix (many rows of J t can be zero for any ({hat {mathbf {u}}}_{t|t-1})), the solution can be highly inaccurate and dependent mainly on the predictions using the state model and the initialization.
After generating the states of u t using the state model of (6), we set the negative elements of u t to 0. This is a modelling approximation adopted to avoid the generation of the negative rainfall values for very low rainfall intensities.
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This control structure is derived by using the state space model of the overall ORC system.
In Section 6, by using the state space model in Section 5, we develop a generalized Bayesian inference procedure to estimate unknown parameters and to predict state variables.
This paper considers discrete and differential nonlinear repetitive processes using the state-space model setting.
Simulations using the state-dependent model have been analysed by comparison with measurements.
The solution of a simply-supported delaminated plate is presented using the state-space model and the displacement, strain and stress fields are determined, respectively.
A recent study using state-space models suggested that pre-filtering of ARGOS data improves the model performance [16], and this seems to be confirmed by another recent study which used a classical speed filter prior to using the state-space model [38].
For comparison, the learning curve estimated using the state-space model of learning (Wirth et al., 2003; Smith et al., 2004) is also plotted (Fig. 6C).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com