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where a 6 (0, 1) (f) Stop growing when any one of the following criteria is satisfied: the maximum map size has been reached; the largest neuron quantization error is less than a user specified threshold, A; the map has converged to the specified quantization error 3 Refine the map: Refine the weights of the final SOM architecture with additional training steps until convergence has been reached A few aspects of the growing algorithm above need some explanation These are the constants A, 7 and the maximum map size A good choice for a is 05 The idea of the interpolation step is to assign a weight vector to the new neuron ab such that it removes patterns from the largest quantization error neuron kj in order to reduce the quantization error of neuron kj A value less than 0.

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Labelling these three terms as A, B and C, respectively, i.e. N = A + B + C, then consider: where T and V are the kinetic and potential energy operators, respectively, of the electron one-particle Hamiltonian He. Employing the standard constant-mass kinetic operator then (in an obvious notation) obtain:

Using (13.14), it is thus the case that, when k = 0,

On performing the differentiations:

Now, we know from our discussion of Homer's rule that the property of s is maintained invariant by the assignment fe,5 := k-l , We, therefore, consider an algorithm of the form

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5 will position neuron ab closer to kj, with the chance that more patterns will be removed from neuron kj A value larger than 05 will have the opposite effect The quantization error threshold, A, is important to ensure that a sufficient map size is constructed A small value for A may result in too large a map architecture, while too large a A may result in longer training times to reach a large enough architecture An upper bound on the size of the map is easy to determine: it is simply the number of training patterns, PT This is, however, undesirable The maximum map size is rather expressed as PT, with (3 6 (0, 1) The optimal value of is problem dependent, and care should be taken to ensure that is not too small if a growing SOM is not used.

which can be written as follows:

{ Invariant: O ^ f e ^ N A y = Xs A sx2k = (Zi:k^ Bound function: k } do fc>0 k,s,y := fc-1 , 2xs + a[fc-l] , Y

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Note that the first term, when normalised by the denominator D, merely represents the one-particle kinetic energy of the electron, which coupled together with (w|v|w>} in equation (6.25) gives the one-particle electron energy Ee as defined in equation (6.15). Furthermore, noting that for the problems of interest, i.e. stationary states within semiconductor microstructures, the wave functions are real and that the chain rule gives:

{ y = XM } ,

If this is the case, the final map may not converge to the required quantization error, since the map size will be too small..

Equation (6.25) therefore becomes:

where Y, the value to be assigned to y in the body of the loop, is the missing element. We calculate the appropriate value of Y using the assignment axiom. The specification of the assignment in the body of the loop is given by the Hoare triple:

Integrate, by parts, over dze the first term in the integrand, i.e.:

Applying the assignment axiom, and ignoring the requirements on k and 5 in the postcondition (as they have already been verified), the requirement reduces to A y = Xs A 5x2 k = (Zi:k^

Training of SOMs is slow, due to the large number of weight updates involved (all the weights are updated for standard SOM training) . Several mechanisms have been developed to reduce the number of training calculations, thereby improving speed of convergence. BatchMap is one such mechanism. Other approaches include the following:

Hence, substituting back into equation (6.30) gives:

1 93

The last two terms in the integration over ze obviously cancel out, and in addition, examination of the first term on the right-hand side, shows that this term will not contribute, since whatever the form of the eigenfunction ij)e, it will undoubtedly vanish at the effective infinities p, and v. Therefore:

We now use properties of exponentiation to calculate the appropriate value of the unknown Y:

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