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Performance is possibly the driving force of all organisms. If no attention is given to improve performance, the quality of life will not improve. Similarly, performance is the most important aspect that has to be considered when an artificial neural network is being designed. The performance of an artificial NN is not just measured as the accuracy achieved by the network, but aspects such as computational complexity and convergence characteristics are just as important. These measures and other measures that quantify performance are discussed in Section 7.1, with specific reference to supervised networks. The design of NNs for optimal performance requires careful consideration of several factors that influence network performance. In the early stages of NN research and applications, the design of NNs was basically done by following the intuitive feelings of an expert user, or by following rules of thumb. The vast number of theoretical analysis of NNs made it possible to better understand the working of NNs - to unravel the "black box". These insights helped to design NNs with improved performance. Factors that influence the performance of NNs are discussed in Section 7.3. Although the focus of this chapter is on supervised learning, several ideas can be extrapolated to unsupervised learning NNs.

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where F is the electric field strength and L is the superlattice period. The conduction and valence band states thus form a series of steps similar to a 'ladder'. In a bipolar system the hole wave functions tend to localise more rapidly than those of the electron's, hence a regime exists where a hole wave function centred in one particular well has a significant overlap with the electron wave functions from adjacent wells, in addition to its own. Therefore, the photoluminescence emission

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Because this is a tighter requirement and just as easy to achieve, this is the one we choose to implement (Exercise 143 asks you to design an algorithm that meets the non-deterministic specification) In summary, the (still-informal) specification is to determine all array elements that have the K smallest values among the bag of values defined by the array Additionally, we will require that it is possible to identify the Kth smallest value in the array To achieve this, we develop an algorithm that rearranges the array elements to the extent that, on termination, the required array elements are in the initial segment of the array These elements are smaller than all other elements and there are at least K of them However, decreasing their number by omitting the elements with the largest value (among the selected array elements) results in there being fewer than K elements remaining.

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and absorption spectra show a series of lines which represent exciton transitions from the hole in well n with electrons in well ra, and in wells n 1 and n +1, as displayed in Fig. 6.14. Given that the electron and hole energy separations between adjacent wells are proportional to the field, then the electron-hole separation (ignoring the exciton binding energy for now) will also be proportional to the field, as displayed in Fig. 6.15 (left). The addition of the exciton binding energy in this simplistic explanation complicates the situation slightly (see Fig. 6.15 (right)), but by the time that moderate electric fields are present, the spectral lines are equally spaced and radiating from the zerofield point. Mendez et al. [146] have observed very similar behaviour in a related system, namely a 30 A GaAs/35 A Ga0.65Al0.35As superlattice. Figure 6.15 (right), however, does not tell the whole story. While it does indicate the position of the spectral lines, it does not give any information about their intensities. The oscillator strength (defined as follows) gives a measure of the expected intensities of the photoluminescence emission and photoluminescence excitation absorption lines:

Formally, our algorithm rearranges the array elements so that, on termination, there are indices 5 and I with the properties that.

LEARNING)

Figure 6.16 displays the oscillator strength as a function of the applied electric field for the three excitonic transitions of interest. At zero field, all eigenstates have definite parity and hence the |en+i} > |hn) and |e n 1 } > |hn) transitions are forbidden; this result arises naturally from the analysis and is demonstrated numerically in the figure by a zero oscillator strength. As the field is increased, the system passes through the region of 'miniband breakup', with the effect of the symmetry breaking induced by the field being to allow the previously forbidden transitions. At larger fields still, the one-particle wave functions will tend towards complete localisation in a single

A (V ij : 0^ i <s A s^ A (V ij : 0^i<l A l^j <N : A (V ij : 5< i< I A s^j <l : a[i] ~a[j}} .

Figure 6.15 The excitonic transition energies characteristic of a Stark Ladder; (left) without exciton binding energies; (right) with exciton binding energies

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