User`s guide
Algorithms for Recursive Estimation
horizon of this alg orithm . Measurements older than
τ
λ
=
−
1
1
typically carry a
weight that is less than about 0.3.
λ
is called the forgetting factor and typically h a s a po siti ve value between
0.97 and 0.995.
Note In the linear regres sio n case, th e forgetting factor algorithm is
known as the recursive least-squares (RLS) algorithm. The forgetting factor
algorithm for
λ
= 1 is equivalent to the Kalman filter algorithm with R
1
=0
and R
2
=1. For more information about the Kalman filter algorithm, see
“Kalman Filter Algorithm” on page 7-8.
Using the Forgetting Factor Algorithm
The general syntax for the command described in “Algorithms for Recursive
Estimation” on page 7-6 is the following:
[params,y_hat]=command(data,nn,adm,ad g)
To specify the forgetting factor algorithm, set adm to 'ff' and adg to the
value of the forgettin g factor
λ
(described in “Mathematics of the Forgetting
Factor Algorithm” on page 7-10).
Tip
λ
typically ha s a positive value from 0.9 7 to 0.995.
Unnormalized and Normalized Gradient Algorithms
• “Mathematics of the Unnormalized and Normalized Gradient Algorithm”
on page 7-12
• “Using the Unnormalized and Normalized Gradient Algorithms” on page
7-12
7-11