Calibration of nonlinear solid-state sensor arrays using multivariate regression techniques
| Autor: | W. Patrick. Carey, Sinclair S. Yee |
|---|---|
| Rok vydání: | 1992 |
| Předmět: |
Multivariate statistics
Multivariate adaptive regression splines Metals and Alloys Nonparametric statistics Regression analysis Condensed Matter Physics Surfaces Coatings and Films Electronic Optical and Magnetic Materials Nonparametric regression Nonlinear system Projection pursuit regression Parametric model Materials Chemistry Statistics::Methodology Electrical and Electronic Engineering Instrumentation Algorithm Mathematics |
| Zdroj: | Sensors and Actuators B: Chemical. 9:113-122 |
| ISSN: | 0925-4005 |
| DOI: | 10.1016/0925-4005(92)80203-a |
| Popis: | The application of an array of eight Taguchi gas sensors to analyze two- and three-component mixture sets of toluene, benzene, acetone, and trichlorethylene is presented. The calibration of each mixture set is performed by two linear-based parametric modeling techniques, partial least-squares and nonlinear partial least-squares, and two nonparametric modeling methods, multivariate adaptive regression splines and projection pursuit regression. The overall ability of nonparametric techniques to calibrate arrays of nonlinear responding sensors and predict future samples is much better than that of linear parametric models. The average prediction error for the nonparametric techniques is approximately half of that for the linear methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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