
Accession Number : AD0711815
Title : ADAPTIVE ESTIMATION WITH MUTUALLY CORRELATED TRAINING SAMPLES.
Descriptive Note : Technical rept.,
Corporate Author : STANFORD UNIV CALIF STANFORD ELECTRONICS LABS
Personal Author(s) : Daniell,Thomas Piatt
Report Date : AUG 1968
Pagination or Media Count : 68
Abstract : The linear leastmeansquare estimate of one element from a sequence of scalar random variables given an observation of the corresponding element from a sequence of vectorvalued random variables (data) is well known. Computation of the estimate requires knowledge of the data correlation matrix. Algorithms have been proposed for iterative determination of the estimate when the data correlation matrix is unknown. These algorithms are easy to implement, require little storage, and are suitable for realtime processing. Past convergence studies of these algorithms have assumed that the data vectors were mutually independent. (Author)
Descriptors : (*ADAPTIVE SYSTEMS, MATHEMATICAL PREDICTION), CORRELATION TECHNIQUES, SAMPLING, ADAPTIVE SYSTEMS, LEAST SQUARES METHOD, RANDOM VARIABLES, VECTOR ANALYSIS, ALGORITHMS, MATRICES(MATHEMATICS), ITERATIONS, CONVERGENCE, REAL TIME, OPTIMIZATION, MEASURE THEORY, DISTRIBUTION FUNCTIONS
Subject Categories : Operations Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE