Author : Chengliang Huang
Affiliation :
1Department of Electrical and Computer Engineering, Ryerson University
Toronto
Country :
Canada
Category : Computer Science & Information Technology
Volume, Issue,
Month, Year
: 6, 5, November, 2016
Abstract
EM
algorithm is popular in maximum likelihood estimation of parameters for
state-space models. However, extant approaches for the realization of EM
algorithm are still not able to fulfill the task of identification systems,
which have external inputs and constrained parameters. In this paper, we
propose new approaches for both initial guessing and MLE of the parameters of a
constrained state-space model with an external input. Using weighted least
square for the initial guess and the partial differentiation of the joint
log-likelihood function for the EM algorithm, we estimate the parameters and
compare the estimated values with the “actual” values, which are set to
generate simulation data. Moreover, asymptotic variances of the estimated
parameters are calculated when the sample size is large, while statistics of
the estimated parameters are obtained through bootstrapping when the sample
size issmall. The results demonstrate that the estimated values are close to
the “actual” values.Consequently, our approaches are promising and can applied
in future research.
Keyword :
State-Space Model, Maximum Likelihood Estimation, Expectation
Maximization Algorithm, Kalman filtering and smoothing, Asymptotic variances,
Bootstrapping
For More Details : https://airccj.org/CSCP/vol6/csit65206.pdf
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