Accession Number : AD0699219
Title : OPTIMAL ADAPTIVE ESTIMATION: STRUCTURE AND PARAMETER ADAPTATION. PART I. LINEAR MODELS.
Descriptive Note : Technical rept.,
Corporate Author : TEXAS UNIV AUSTIN ELECTRONICS RESEARCH CENTER
Personal Author(s) : Lainiotis,D. G.
Report Date : 05 SEP 1969
Pagination or Media Count : 36
Abstract : A Bayesian approach to optimal adatpive estimation with continuous as well as discrete data is presented. Both structure and parameter adaptation are considered and specific recursive adaptation algorithms are derived for gaussian process models and linear dynamics. Specifically, for the class of adaptive estimation problems with linear dynamic models and gaussian excitations, a form of the 'partition' theorem is given that is applicable both for structure and parameter adaptation. The 'partition' or 'decomposition' theorem effects the partition of the essentially nonlinear estimation problem into two parts, a linear non-adaptive part consisting of ordinary Kalman estimators and a nonlinear part that incorporates the adaptive or learning nature of the adaptive estimator. In addition, simple performance measures are introduced for the on-line performance evaluation of the adaptive estimator. The on-line performance measure utilize quantities available from the adaptive estimator and hence a minimum of additional computational effort is required for evaluation. Adaptive estimators are given for filtering, prediction, as well as smoothing. (Author)
Descriptors : (*INFORMATION THEORY, *DECISION THEORY), CONTROL SYSTEMS, ADAPTIVE SYSTEMS, MATHEMATICAL MODELS, ALGORITHMS, THEOREMS
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE