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An adaptive filter for time‐varying‐parameter models
Author(s) -
Abutaleb A.,
Papaioannou M.
Publication year - 1990
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.4480040502
Subject(s) - filter (signal processing) , mathematics , control theory (sociology) , kernel adaptive filter , adaptive filter , consistency (knowledge bases) , pontryagin's minimum principle , filter design , kalman filter , computer science , mathematical optimization , algorithm , optimal control , statistics , artificial intelligence , control (management) , geometry , computer vision
A non‐linear adaptive filter is introduced and applied to the classical problem of estimating time‐varying‐parameter linear regression models with unknown error variances and a time‐varying transition matrix. The filter is basically a new result in what is known as Sridhar filtering theory. In deriving the filter, which we call the ‘Pontryagin filter’, the Pontryagin minimum principle and the method of invariant imbedding were used. The properties (bias and consistency) of the estimates of the time‐varying parameters are then established.