An alternative bias estimation technique and an application of the least squares
technique in multiple linear connections.
If [P.sub.m] and [P.sub.m+1] are [H.sub.m.sup.T] [Q.sub.m.sup.-1] [H.sub.m] and [H.sub.m+1.sup.T] [Q.sub.m+1.sup.-1] [H.sub.m+1.sup.T] separately, the recursive least squares
estimation for the load inertia can be expressed in (9) when the number m+1 set of test data is observed.
These are the required least squares
estimates of a and b that minimise E in equation (1).
The least squares
solution with least norm is [x.sub.0] = [N.sup.[dagger]]c.
To improve the convergence speed, we derive a least squares
based iterative (LSI) identification algorithm.
Furthermore, we also defined a central window on k-space, in which missing data points are still interpolated by weights generated from the conventional least squares
The coefficient of the variable X1 is negative, which is inconsistent with the actual situation, in order to eliminate the multicollinearity among variables, using partial least squares
This task can be accomplished by least squares
method using the extended observation equation
The Beavers-Joseph-Saffman interface conditions are treated as an extra least squares
functional, while boundary conditions are imposed into solution spaces.
We generally used the method of least square
to estimate the parameter of linear models.
This was supported by a difference in least squares
means of 0.062 kg (P less than 0.0001) in favor of males for this trait.
Hu, "Iterative and recursive least squares
estimation algorithms for moving average systems," Simulation Modelling Practice and Theory, vol.