Calibration of nonlinear variable loads based on manifold learning
In this work, we present a method for calibrating non-linear variable impedances based on the manifold-learning technique. This approach circumvents the dependency on the analytical model of the device, and works under the assumption that the impedance values come from a ”black box” controlled by a...
Guardado en:
| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/154145 |
| Aporte de: |
| Sumario: | In this work, we present a method for calibrating non-linear variable impedances based on the manifold-learning technique. This approach circumvents the dependency on the analytical model of the device, and works under the assumption that the impedance values come from a ”black box” controlled by a number of independent parameters. The goal of the calibration is to recover the unknown control parameters that set the load into the desired impedance states. We tested the proposed procedure first on a simulated example and then on the prototype presented in [1] at a frequency of 1575.42 MHz. The results based on both simulated and real data showed accurate recovery of the control parameters. |
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