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We introdrice the notation to be used in the following chapters and describe the MLP model, illustrated in Figure 2.1. We end with a brief discussion of how a function approximation model like an MLP can be used for classification. = { ~ ~ , t ~ } where , each z, E : = ~ , is a Say we have a set of training data, feature vector of length and { 1 , . . . ,Q} is the corresponding class label. We form a data matrix containing the training data

7-80

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AM polymerization offers the potential for suppressing side reactions and achieving living polymerization with the ability to control MW and achieve high molecular weights.

Lipases catalyze the polymerization of lactones [Duda et al., 2002; Gross et al., 2001; Kobayashi, 1999; Kobayashi et al., 2001]. The reaction mechanism is similar to that for the enzymatic polymerization of hydroxyacids (Sec. 2-17a-2). Lipase reacts with lactone to produce enzyme-activated hydroxyacid and some of the latter reacts with water to produce hydroxyacid (Eqs. 7-81). Hydroxyacid and enzyme-activated hydroxyacid react to initiate polymerization (Eq. 7-82). Propagation proceeds by nucleophilic attack of

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where ., + ( I , x , , l , . . . ,x n p ) . Hence the data matrix consists of N observations, c each of length + 1, and each containing a feature vector augmented by the addition of a 1 in the first coordinate position. The 1 is multiplied by a parameter known the bias, which is equivalent to the intercept or 40 term in a linear regression. Comparing Figure 2.1 and Figure 1.2 (p. 5), we can see that the essential structure of the MLP is that of perceptrons interconnected in layers, with the outputs from one layer forming the inputs to the next layer.

the hydroxyl end group of the propagating chain on the enzyme-activated hydroxyacid (Eq. 7-83a).

7-81

7-82

5

Polymerization and copolymerization of the two 1,4-dioxane-2,5-diones (dilactones), glycolide and lactide (XLIX with R H and CH3 , respectively) proceeds using anionic initiators;

A Statistical Approach to Neural Networks for Pattern Recognition by Robert A. Dunne Copyright @ 2007 John Wiley & Sons, Inc.

7-84

cationic initiators are not as useful [Bero et al., 1990; Chamberlain et al., 2001; Chisholm et al., 2001; Kowalski et al., 1998; Kricheldorf and Kreiser-Saunders, 1990; Kricheldorf et al., 1987a,b, 2000; Leenslag and Pennings, 1987; Shibasaki et al., 2000; Stridsberg et al., 2000]. The polymerization rates are generally lower than those for lactones. Polylactide is of interest because it is both biocompatible and biodegradable. It has been used for absorbable sutures and has the potential for other biomedical applications such as drug delivery. Polymerization of a cyclic carbonate ester yields a linear polycarbonate [Kuhling et al., 1989; Rokicki, 2000]. For example, the cyclic oligomer (m 2 20 in Eq. 7-85) of the

C(CH3)2

Figure In the molti-layer perceptron model, each operational unit, represented in the figure by a circle with a number of input lines, is a perceptron. T h e outputs of the perceptrons on one layer form the inputs t o the perceptrons on the next layer. Each component of the input pattern is presented separately at the nodes of the input layer; the components are weighted, summed, and passed through the perceptron activation function. T h e outputs of the first, or hidden, layer form the inputs to the nodes of the output layer. While the figure has only two layers of perceptrons, it is of course possible to have an arbitrary number of such layers.

C(CH3)2

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