Maximum Likelihood Identification of Linear Discrete Stochastic Systems,
Abstract
The method of maximum likelihood is applied to the identification of parameters in systems described by linear difference equations. The equations are assumed to be completely known except for the state variable coefficients, i.e., the state transition matrix, and, in certain situations, the initial conditions. The estimates are based on known normal operating input and on output measurements corrupted by additive gaussian noise. Maximum likelihood estimators of the parameters are developed for the following four cases: initial condition known, initial condition unknown parameter, initial condition unknown random variable, and an equivalent 'equation-error' model configuration. Finite sample and asymptotic properties of the estimators as well as computational aspects are investigated. The study is oriented toward real time applications. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 1972
- Accession Number
- AD0755954
Entities
People
- Albert J. Glassman
Organizations
- University of California, Los Angeles