Sentence examples for uncertainty weight from inspiring English sources

Both unstructured and diagonal uncertainty are considered as well as various structures of the uncertainty weight matrix.

For the case with no a priori information, it is shown that a nonconservative uncertainty description can be obtained by minimizing the magnitude of the determinant of the uncertainty weight matrix subject to the output-matching condition.

It is shown that through characterizing frequency response from input to temperature at each spatial point, a distributed parameter system with nominal model and additive uncertainty weight, both of which are real rational, is reconstructed using knowledge of the eigenstructure.

This paper proposes a method based on computing a mean (nominal) plant, uncertainty weight based on the deviations of the extreme plants from the mean plant and performance weights capturing desired performance.

Consequently the respective uncertainty weight diminishes, and the interval tightens up around d ij.

In this phase we employ the MCS to assess the uncertainty weight space, where weights are expressed using probability density functions.

Model uncertainties are identified using artificial neural networks, and an uncertainty weighting function is approximated for LPV control synthesis.

The procedure is illustrated by estimation of uncertainty weights and design of μ-optimal controllers for a distillation column.

Using the model, a μ-controller is synthesized, in which model uncertainties are quantified by uncertainty weights included in the control design process.

The linear model is qualitatively compared to the original model and the discrepancies are quantified in terms of uncertainty weights and included in the control design process.

Modern robust control (known as H∞) theory represents an efficient possibility to solve robustness requirements in a general way based on exact mathematical formulation (Linear Matrix Inequalities) combined with knowledge-based expertise (through real patient data, uncertainty weighting functions can be formulated).