Title :
System identification based on variational Bayes method and the invariance under coordinate transformations
Author :
Fujimoto, Kenji ; Satoh, Akinori ; Fukunaga, Shuichi
Author_Institution :
Dept. of Mech. Sci. & Eng., Nagoya Univ., Nagoya, Japan
Abstract :
This paper proposes a parameter estimation method for state-space models based on the variational Bayes method. We adopt the same form of functions as the prior and posterior probability distributions so that we can be used it iteratively to obtain accurate estimation whereas the existing algorithms cannot be used iteratively. Furthermore, the proposed algorithm is invariant under coordinate transformations, in the sense that the posterior probabilities of state-space models similar to each other are equivalent. Moreover, a numerical example demonstrates the effectiveness of the proposed method.
Keywords :
Bayes methods; parameter estimation; state-space methods; statistical distributions; coordinate transformations; parameter estimation; probability distributions; state-space models; system identification; variational Bayes method; Bayesian methods; Educational institutions; Equations; Estimation; Kalman filters; Mathematical model; State-space methods;
Conference_Titel :
Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-61284-800-6
Electronic_ISBN :
0743-1546
DOI :
10.1109/CDC.2011.6160563