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 least-mean-square estimate of one element from a sequence of scalar random variables given an observation of the corresponding element from a sequence of vector-valued 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 real-time 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