OPTIMAL ADAPTIVE FILTER REALIZATIONS FOR SAMPLE STOCHASTIC PROCESSES WITH AN UNKNOWN PARAMETER

被引：10

作者：

HILBORN, CG

LAINIOTIS, DG

机构：

[1] Dept. of Elec. Engrg., University of Texas, Austin, Tex.

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 1969年 / AC14卷 / 06期

关键词：

D O I：

10.1109/TAC.1969.1099328

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Techniques are given for realizing optimal learning systems for filtering a sampled stochastic process in the presence of an unknown constant or time-varying parameter. It is shown how the nonlinear Bayes optimal (quadratic sense) adaptive filters can be directly realized for continuous parameter spaces by real-time analog systems. Examples are given for both constant and time-varying unknown parameters. Copyright © 1970 by The Institute of Electrical and Electronics Engineers, Inc.

引用

页码：767 / +

页数：1

共 11 条

[1]

BALAKRISHNAN AV, 1962, P NATL TELEMETERING, V2

[2] THE PREDICTION ERROR OF STATIONARY GAUSSIAN TIME-SERIES OF UNKNOWN COVARIANCE [J].

DAVISSON, LD .

IEEE TRANSACTIONS ON INFORMATION THEORY, 1965, 11 (04) :527-532

[3]

DAVISSON LD, 1965 P NEC

[4]

GABOR D, 1961, P IEE LONDON, V108

[5]

GERSHO A, 1963, EE559 CORN U TECH RE

[6]

GERSHO A, 1963, THESIS CORNELL U

[7] OPTIMAL ESTIMATION IN PRESENCE OF UNKNOWN PARAMETERS [J].

HILBORN, CG ;

LAINIOTI.DG .

IEEE TRANSACTIONS ON SYSTEMS SCIENCE AND CYBERNETICS, 1969, SSC5 (01) :38-&

[8]

MAGIL DT, 1963, SEL63143 STANF EL LA

[9]

SHAW LG, 1960, 21062 STANF EL LABS

[10]

SPRAGINS JD, G1037 STANF EL LABS

← 1 2 →