Robust maximum-likelihood estimation of multivariable dynamic systems
نویسندگان
چکیده
منابع مشابه
Robust maximum-likelihood estimation of multivariable dynamic systems
This paper examines the problem of estimating linear time-invariant state-space system models. In particular it addresses the parametrization and numerical robustness concerns that arise in the multivariable case. These difficulties are well recognised in the literature, resulting (for example) in extensive study of subspace based techniques, as well as recent interest in “data driven” local co...
متن کاملRobust and Simple Algorithms for Maximum Likelihood Estimation of Multivariable Systems
Abstract: This paper presents novel algorithms for the estimation of dynamic systems. These new methods offer several advantages of being parameterisation free, numerically robust, convergent to statistically optimal estimates, and applicable in a simple fashion to a wide range of multivariable, non-linear and time varying problems. The key tool underlying the new techniques presented here is t...
متن کاملMaximum Likelihood Recursive Least Squares Estimation for Multivariable Systems
This paper discusses parameter estimation problems of the multivariable systems described by input–output difference equations. We decompose a multivariable system to several subsystems according to the number of the outputs. Based on the maximum likelihood principle, a maximum likelihood-based recursive least squares algorithm is derived to estimate the parameters of each subsystem. Finally, t...
متن کاملRobust Pitch Estimation Using l1-regularized Maximum Likelihood Estimation
This paper presents a new method of robust pitch estimation using sparsity-based estimation techniques. The method is developed based on sparse representation of a temporalspectral pitch feature. The robust pitch feature is obtained by accumulating spectral peaks over consecutive frames. It is expressed as a sparse linear combination of an over-complete set of peak spectrum exemplars. The proba...
متن کاملMaximum Likelihood Estimation of Parameters in Generalized Functional Linear Model
Sometimes, in practice, data are a function of another variable, which is called functional data. If the scalar response variable is categorical or discrete, and the covariates are functional, then a generalized functional linear model is used to analyze this type of data. In this paper, a truncated generalized functional linear model is studied and a maximum likelihood approach is used to esti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Automatica
سال: 2005
ISSN: 0005-1098
DOI: 10.1016/j.automatica.2005.05.008