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The quantifier notation extends a binary operator, e say, to an arbitrary bag of values, the bag being defined by a function (the term) acting on a set (the range) The form of a quantified expression is (@bv type:range:term) , where is the quantifier, bv is the dummy or bound variable and type is its type, range defines a subset of the type of the dummy over which the dummy ranges, and term defines a function on the range The value of the quantification is the result of applying the operator to all the values generated by evaluating the term at all instances of the dummy in the range Strictly, the type of the dummy should always be explicitly stated because the information can be important (as in, for example, the stronger relation between the less-than and at-most orderings on integers compared with their properties on reals).

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Fig. 5.5 shows the results of calculations for a range of donor positions across the same 60 A CdTe quantum well. The Cd 1 _ x Mn x Te system has been chosen to illustrate the method merely because the binding energy is larger, as calculated earlier, so it seems a bit more interesting than the more common Ga 1 _ x Al x As system. In fact, Cd 1 _ x Mn x Te is quite similar to Ga 1 _ x Al x As ; i.e. it is a direct material, with a similar bandgap, and CdTe forms type I quantum wells with a valence band offset similar to that of GaAs/Ga1_xAlxAs. None of the results obtained are particularly features of the choice of material, and re-emphasing the aim of this present book, it is the theory and computational methods and the generic deductions that are important, and not the absolute values of the calculations. As mentioned already, all of the necessary tools are provided for the reader to quite quickly repeat the calculations for the material system of interest to them. Therefore, returning to Fig. 5.5, as probably expected the electron energy E is lowest (and therefore the donor binding energy EDo highest) for a donor at the centre of the well. It would be expected a priori that the electron energy would return to the value without the donor present, as the donor is moved further and further away from the well, i.e.

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[Prechelt 1995] L Prechelt, Adaptive Parameter Pruning in Neural Networks, Technical Report TR-95-009, International Computer Science Institute, Berkeley, California, 1995. [Rechenberg 1973] I Rechenberg, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der Biologischen Evolution, Frammann-Holzboog Verlag, Stuttgart, 1973. [Rechenberg 1994] I Rechenberg, Evolution Strategy, in JM Zurada, R Marks II, C Robinson (eds), Computational Intelligence: Imitating Life, IEEE Press, 1994, pp 147 159. [Reed 1993] R Reed, Pruning Algorithms - A Survey, IEEE Transactions on Neural Networks, Vol 4, No 5, 1993, pp 740 747. [Reed et al. 1995] R Reed, RJ Marks II, S Oh, Similarities of Error Regularization, Sigmoid Gain Scaling, Target Smoothing, and Training with Jitter, IEEE Transactions on Neural Networks, Vol 6, 1995, pp 529 538. [Reynolds 1979] RG Reynolds, An Adaptive Computer Model of the Evolution of Agriculture, PhD Thesis, University of Michigan, Michgan, 1979. [Reynolds 1991] RG Reynolds, Version Space Controlled Genetic Algorithms, Proceedings of the Second Annual Conference on Artificial Intelligence Simulation and Planning in High Autonomy Systems, IEEE Press, 1991, pp 6 14. [Reynolds 1994] RG Reynolds, An Introduction to Cultural Algorithms, Proceedings of the Third Annual Conference on Evolutionary Computing, 1994, pp 131 139. [Reynolds 1999] RG Reynolds, Cultural Algorithms: Theory and Application, in D Corne, M Dorigo, F Glover (eds), New Ideas in Optimization, MacGraw-Hill, 1999, pp 367 366. [Ripley 1996] BD Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996. [Robel 1994] A Robel, The Dynamic Pattern Selection Algorithm: Effective Training and Controlled Generalization of Backpropagation Neural Networks, Technical Report, Institut fur Angewandte Informatik, Technische Universitat Berlin, March 1994, pp 497 500. [Rosen and Goodwin 1997] BE Rosen, JM Goodwin, Optimizing Neural Networks Using Very Fast Simulated Annealing, Neural, Parallel & Scientific Computations, Vol 5, No 3, 1997, pp 383 392.

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