Nonlinear system identification is discussed in a mixed set-membership and statistical setting. A Markov chain Monte Carlo (MCMC) approach is proposed that estimates the feasible parameter set, the minimum volume outer-bounding ellipsoid and the minimum variance estimate. The proposed algorithm is proved to be convergent and enjoys some desirable properties. Further, its computational complexity and numerical accuracy are studied.
A Markov Chain Monte Carlo Approach to Nonlinear Parametric System Identification
Tempo R
2015
Abstract
Nonlinear system identification is discussed in a mixed set-membership and statistical setting. A Markov chain Monte Carlo (MCMC) approach is proposed that estimates the feasible parameter set, the minimum volume outer-bounding ellipsoid and the minimum variance estimate. The proposed algorithm is proved to be convergent and enjoys some desirable properties. Further, its computational complexity and numerical accuracy are studied.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
