Rapid continuous-time identification of linear and nonlinear systems using modulation function approaches
At the present, system identification through modulation functions has a wide range of methods. Many of them have reached maturity levels that surpass customary Kalmanfilter approaches for discrete-time identification. In this thesis, the modulation function technique is analyzed in view of its r...
| Autor Principal: | Cieza Aguirre, Oscar Benjamín |
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| Formato: | Tesis de Maestría |
| Idioma: | Inglés |
| Publicado: |
Pontificia Universidad Católica del Perú
2017
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| Materias: | |
| Acceso en línea: |
http://tesis.pucp.edu.pe/repositorio/handle/123456789/8123 |
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| Sumario: |
At the present, system identification through modulation functions has a wide range of
methods. Many of them have reached maturity levels that surpass customary Kalmanfilter
approaches for discrete-time identification. In this thesis, the modulation function
technique is analyzed in view of its real-time capability, as well as the possible unification
of the modulation function methods based on the frequency spectrum, and ability
to deal with nonlinearities. Besides, to increase the rate of convergence, the optimal
parameter estimation with constraints of Byrski et al. [BFN03] is applied on integrable
and convolvable systems. Furthermore, the modulated white Gaussian noise influence
on linear systems is examined. The proposed methods together with the Loab-Cahen
modulation functions are compared in performance for linear and convolvable systems
concerning three different inputs, three normalizations, identification parameters and
computational cost. |
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