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And the worst angle output error within ± 0.2° is achieved.
The closed-loop output error dynamics is required to be linear and noninteractive.
However, imprecise forecasting inflow may lead to output error and hinder implementation of power generation schedules.
The Output Error Method is related to an optimization problem based on a multimodal criterion.
A pragmatic approach is proposed in an output error framework, from sampled input and output data.
A weighted non-linear least squares algorithm then minimizes the output error.
The output error (OE) method is used to estimate model parameters.
Detailed analysis about the relationship between noise power and output error is investigated.
The procedure merges output error control with Hessian-based anisotropic grid adaptation.
Moreover frequency output error of the sensor due to non-linear vibration is also examined.
Output error dynamics can thus be derived as a stable first-order filter driven by parameter error vectors.
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