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The optimization method used to determine weight adjustments has a large influence on the performance of NNs. While GD is a very popular optimization method, GD is plagued by slow convergence and susceptibility to local minima (as introduced and discussed in Section 3.2.2). Improvements of GD have been made to address these problems, for example, the addition of the momentum term. Also, second-order derivatives of the objective function have been used to compute weight updates. In doing so, more information about the structure of the error surface is used to direct weight changes. The reader is referred to [Battiti 1992, Becker and Le Cun 1988, M011er 1993]. Other approaches to improve NN training are to use global optimization algorithms instead of local optimization algorithms, for example simulated annealing [Rosen and Goodwin 1997], genetic algorithms [Engelbrecht and Ismail 1999, Janson and Frenzel 1993, Kuscu and Thornton 1994], particle swarm optimization algorithms [Corne et al. 1999, Eberhart et al. 1996, Engelbrecht and Ismail 1999, Van den Bergh 1999, Van den Bergh and Engelbrecht 2000], and LeapFrog optimization [Snyman 1982, Snyman 1983, Engelbrecht and Ismail 1999].

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Multiplying both sides by 2r(6r)2 gives:

r , m := P,Q ;

Gathering terms in w(r + 8r), w(r), and w(r 8r), then:

{ Invariant: (3k:0^k r-m } = Qx2 k ) Bound function:

Referring to one of Ockham's statements, if several networks fit the training set equally well, then the simplest network (i.e. the network which has the smallest number of weights) will on average give the best generalization performance [Thodberg 1991]. This hypothesis has been investigated and confirmed by Sietsma

Then finally, obtain:

do r ^ m - m := 2xm od ;

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which is an iterative shooting equation, similar to those met earlier in 3. It can be solved according to the standard boundary condition, w(r} > 0 as r > 8, as before. Now in regions of constant potential, the wave functions are in general continuous, and therefore for the particular case of a straight line perpendicular to and through the wire (x-) axis, the wave function must also be continuous. Thus, when crossing the wire axis, the radial component of the wave function if)(r) must have a derivative of zero, i.e. a local maxima or minima. This allows the iterative starting conditions to be chosen as:

{ Invariant: ( K r < m A (3d :: P = Qxd + r) A < 3 f c : C K f c : r a = Qx2 k ) Bound function: m } 0 ^ r < 2 x m A (3d :: P = A (3k:0^k:m = Qx2 f e ) } if r<m - skip n r^ra - r := r-m

LEARNING)

These can look a little simplistic, but in the limit of decreasing step length Sr, the maxima or minima is clearly flat.

do ra^Q -

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Figure 8.13 The radial component of the wave function w(r) for the lowest two eigenstates in a finite-barrier quantum wire with radius 300 A of circular cross-section

m := m + 2 ;

and Dow [Sietsma and Dow 1991] A network with too many free parameters may actually memorize training patterns and may also accurately fit the noise embedded in the training data, leading to bad generalization Overfitting can thus be prevented by reducing the size of the network through elimination of individual weights or units The objective is therefore to balance the complexity of the network with goodness of fit of the true function This process is referred to as architecture selection Several approaches have been developed to select the optimal architecture, ie regularization, network construction (growing) and pruning These approaches will be overviewed in more detail below Learning is not just perceived as finding the optimal weight values, but also finding the optimal architecture However, it is not always obvious what is the best architecture Finding the ultimate best architecture requires a search of all possible architectures.

Fig. 8.12 displays the results of calculations of the electron confinement energy versus the wire radius, for a GaAs wire surrounded by Ga0.8Al0.2As, for constant effective mass. As expected, the confinement energy decreases with increasing radius and the odd-parity eigenstate is of higher energy than the even. This latter point is highlighted in Fig. 8.13, which plots the radial motion w(r) for the 300 A radius wire. The even- (n = 1) and odd- (n = 2) parity nature of the eigenstates can clearly be seen.

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