Title :
A Markov Chain Monte Carlo Approach to Nonlinear Parametric System Identification
Author :
Bai, Er-Wei ; Ishii, Hideaki ; Tempo, Roberto
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
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.
Keywords :
Markov processes; Monte Carlo methods; computational complexity; convergence; nonlinear systems; parameter estimation; Markov chain Monte Carlo approach; computational complexity; convergence; minimum variance estimate; mixed set-membership; nonlinear parametric system identification; outer-bounding ellipsoid; parameter set estimation; Approximation methods; Computational complexity; Convergence; Ellipsoids; Noise; Random sequences; Monte Carlo; parameter estimation; system identification;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2014.2380997
