Next we prove that equation 3.4:

where and , is the solution to the system of the following equations:

We assume here that the weight vectors are column vectors with the same dimensionality as .

We know that the equations do not have a unique solution if the weight vectors are identical. In that case the correlation between the weight vectors would equal one. We assume that this degenerate case can be omitted, and we always have .

The derivation is very similar to the previous one. At first we combine equations A.10, A.12 and A.13, which yields

We can further manipulate the norm appearing on the right hand side of the equation. We denote the norm by .

Using the notations , and , and substituting we can write

Let us denote the square matrix by *S*. We can now write

Note that because *S* is positive definite (we assumed ),
is a parabolic function.

We first find the solution without the constraints
A.14 and A.15. If is the
solution to the equation, then the gradient of at point
has to be a zero vector . The gradient of *N* is

and therefore the solution has to satisfy

The computation of yields

and thus the solution without the constraints is

Next we take into consideration the constraints A.14 and A.15. If either of the is less than or equal to zero, then = 0 and the solution to the other output reduces to the case of one neuron, which has already been solved. We can thus assume that both . The constraints bound a rectangular area of allowed outputs. If the unconstrained solution given by equation A.16 lies in that area, it is also the solution to the constrained minimisation. Those cases, where the unconstrained solution does not lie in the allowed area, are left to examine. Due to the symmetry, the solutions to and are identical except for the interchanged indices. We can therefore consider only the solution to . Let us see what happens if . It is evident that the solutions to both of the outputs in equation A.16 exceed the boundaries:

Because is parabolic, the best constrained solution lies in the corner of the allowed area: and .

Now only those cases are left where , and *c* > 0. Let us
consider what happens if equation A.16 gives a solution
. We now that , and therefore . We see that
if the unconstrained solution to one of the outputs is under the
bound, then the other is over, and vice versa. Again the constrained
solution is found in the corner of the allowed area. This time one of
the outputs is zero and the other is . The solution to the case
can thus be formulated

Collecting together all the different cases yields the equation 3.4. This concludes the proof.

Thu May 9 14:06:29 DST 1996