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One of the most important steps in using a NN to solve real-world problems is to collect and transform data into a form acceptable to the NN. The first step is to decide on what the inputs and the outputs are. Obviously irrelevant inputs should be excluded. Section 7.3.5 discusses ways in which the NN can decide itself which inputs are irrelevant. The second step is to process the data in order to remove outliers, handle missing data, transform non-numeric data to numeric data and to scale the data into the active range of the activation functions used. Each of these aspects are discussed below: Missing Values It is common that real-world data sets have missing values for input parameters. NNs need a value for each of the input parameters. Therefor, something has to be done with missing values. The following options exist: Remove the entire pattern if it has a missing value. While pattern removal solves the missing value problem, other problems are introduced: (1) the available information for training is reduced which can be a problem if data is already limited, and (2) important information may be lost.

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Using equations (7.20) and (7.21), the stress component a\ can be expressed in terms of the strain as . Hence, the final form of the in-plane piezoelectric polarisation of GaN reads:

Exercise 15.14. Use (15.6) to calculate P mod 1 and P + 1. D Exercise 15.15. Assume that -Q ^ P < 0. In this case, can you calculate simpler expressions for P mod Q and P + Q from (15.6) D Exercise 15.16. Use (15.7) and (15.12) to simplify (Pmod (Qxn)) modQ, where 0<n. D Exercise 15.17. Show that, for all m and n, rmodQ = n is an invariant of the assignment

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where Cij are the elastic stiffness constants. As the term Cu+ C\i IC^/C^ > 0 is always greater than zero, the sign of the polarisation is determined by the sign of the strain. Hence, P3 is always positive for layers under biaxial compressive strain and negative for layers under tensile strain. A more often used approach for describing piezoelectric properties of crystals than the moduli is that of the piezoelectric constants defined as:

Exercise 15.18. Rather than develop separate algorithms for the cases that 0 ^ P and P < 0, as we did in Section 15.2, an alternative is to express PmodQ and P---Q as functions of ((-(P+l)) modQ) and (-(P+l)) + Q, respectively. (Since p<0 = 0^-(P+1), the existence of such functions means that only the algorithm for positive P is needed.) Calculate these functions using (15.6). D

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Replace each missing value with the average value for that input parameter in the case of continuous values, or with the most frequently occurring value in the case of nominal or discrete values This replacing of missing values introduces no bias For each input parameter that has a missing value, add an additional input unit to indicate patterns for which parameters are missing It can then be determined after training whether the missing values had a significant influence on the performance of the network While missing values present a problem to supervised neural networks, SOMs do not suffer under these problems Missing values do not need to be replaced The BMN for a pattern with missing values is, for example, calculated by ignoring the missing value and the corresponding weight value of the codebook vector in the calculation of the Euclidean distance between the pattern and codebook vector.

where Cji are the elastic stiffness constants. Then equation (7.49) for the piezoelectric polarisation can be rewritten as:

It is convenient to have a rule for reasoning about the mod function alone, without explicit mention of the -r function. This is obtained from (15.6) by existentially quantifying over the variable d. We get that, for all P and all Q, where

In terms of these piezoelectric constants, the polarisation in the wurtzite GaN layer under biaxial strain now reads:

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