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 technique.

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 regression method.

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.