Accession Number : AD0722083

Title :   Optimal Adaptive Estimation: Structure and Parameter Adaptation,

Corporate Author : TEXAS UNIV AUSTIN DEPT OF ELECTRICAL ENGINEERING

Personal Author(s) : Lainiotis,D. G.

Report Date : APR 1971

Pagination or Media Count : 31

Abstract : Optimal structure and parameter adaptive estimators have been obtained for continuous as well as discrete data gaussian process models with linear dynamics. Specifically, the essentially nonlinear adaptive estimators are shown to be decomposable (partition theorem) into two parts, a linear non-adaptive part consisting of a bank of Kalman-Bucy filters, and a nonlinear part that incorporates the adaptive nature of the estimator. The conditional-error-covariance matrix of the estimator is also obtained in a form suitable for on-line performance evaluation. The adaptive estimators are applied to the probelm of state-estimation with nongaussian initial state, to estimation under measurement uncertainly (joint detection-estimation) as well as to system identification. Examples are given of the application of the adaptive estimators to structure and parameter adaptation indicating their applicability to engineering problems. (Author)

Descriptors :   (*CONTROL SYSTEMS, MATHEMATICAL MODELS), (*DECISION THEORY, STOCHASTIC PROCESSES), MATHEMATICAL PREDICTION, NONLINEAR SYSTEMS, OPTIMIZATION, ADAPTIVE SYSTEMS, THEOREMS

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE