Name
Abstract | ||
|---|---|---|
Polynomial regression model is very important in the modeling and characterization of sensors. The uncertainty propagation through the polynomial nonlinearity can only be estimated through numerical simulation or linearization approximation according to the Guide to the expression of Uncertainty in Measurement. This paper developed a general cookbook style guide to derive the analytical expression of uncertainty propagating through the polynomial regression models. The proposed method can be easily incorporated into any computer algebra system for reliable and fast evaluation. Specific expressions are derived explicitly for some of the most commonly used low order polynomial regression models. The framework is applied to a few recently published sensor and measurement system models. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SAS.2014.6798947 | Sensors Applications Symposium |
Keywords | Field | DocType |
measurement systems,measurement uncertainty,polynomials,regression analysis,sensors,algebra system,measurement system models,polynomial regression models,standard uncertainty estimation,uncertainty propagation,polynomial regression,uncertainty,analytic solution | Applied mathematics,Mathematical optimization,Propagation of uncertainty,Polynomial,Computer science,Polynomial regression,Measurement uncertainty,Sensitivity analysis,Theoretical computer science,Uncertainty analysis,Polynomial chaos,Linearization | Conference |
Citations | PageRank | References |
1 | 0.38 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rajan, A. | 1 | 1 | 0.38 |
| Ye Chow Kuang | 2 | 72 | 19.81 |
| Ooi, M.P.-L. | 3 | 1 | 0.38 |
| Demidenko, S. | 4 | 8 | 1.37 |