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Knowledge Discovery from Data Streams
book

Knowledge Discovery from Data Streams

by Joao Gama
May 2010
Intermediate to advanced content levelIntermediate to advanced
255 pages
8h 11m
English
Chapman and Hall/CRC
Content preview from Knowledge Discovery from Data Streams
172 Knowledge Discovery from Data Streams
The Kalman filter estimates the state of the system using a set of recursive
equations. These equations are divided into two groups: time update equations
and measurement update equations. The time update equations are respon-
sible for projecting forward (in time) the current state and error covariance
estimates to obtain the a priori estimates for the next time step.
ˆx
k
= Aˆx
k1
(11.8)
P
k
= AP
k1
A
T
+ Q (11.9)
The measurement update equations are responsible for the feedback, i.e.
for incorporating a new measurement into the a priori estimate to obtain an
improved a posteriori estimate.
K
k
=
P
k
H
T
HP
k
H
T
+ R
(11.10) ...
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Publisher Resources

ISBN: 9781439826126