Accession Number : AD0755954

Title :   Maximum Likelihood Identification of Linear Discrete Stochastic Systems,

Corporate Author : CALIFORNIA UNIV LOS ANGELES SCHOOL OF ENGINEERING AND APPLIED SCIENCE

Personal Author(s) : Glassman,Albert J.

Report Date : SEP 1972

Pagination or Media Count : 209

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)

Descriptors :   (*ADAPTIVE CONTROL SYSTEMS, MATHEMATICAL MODELS), LINEAR SYSTEMS, STOCHASTIC PROCESSES, IDENTIFICATION SYSTEMS, DIFFERENCE EQUATIONS, STATISTICAL ANALYSIS, CURVE FITTING, GRAPHICS, RANDOM VARIABLES, REAL TIME

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE