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If the output from the network (the prediction of the time-reversed Slow %K) is below 100 minus thresh, a buy signal is issued As an example, if thresh were set to 80, any predicted,time-reversed Slow %K greater than 80 would result in the posting of a sell signal, and any predicted timereversed Slow %K less than 20 would result in the issuing of a buy signal Finally, there are the two blocks of code used to issue the actual entry orders and to implement the standardized exits These blocks of code are identical to those that have appeared and been discussed in previous chapters Test Methodology for the Reverse Slow %K Model The model is executed with the mode parameter set to 1 to generate a fact set The fact set is loaded into N-TRAIN, a neural network development kit (Scientific.



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Data can take many forms, from simple types passed back from web services to complex formats such as XML. In the previous chapter, you saw how to consume web services from Silverlight and connect to various servers, including ones that live outside your application s host domain and others that communicate over sockets. Once you have data, though, you must process it and/or display it to users. Silverlight provides a DataGrid control, a data binding architecture to connect data to user interface elements, and even item templates for controls like the ListBox to specifically define how each item should appear. On the data-processing side, Silverlight provides a number of classes for working with XML, including Language Integrated Query (LINQ). Another important aspect to data is how to save data on the client. While you can use cookies, Silverlight provides something called isolated storage that provides file system semantics for saving and loading data. Let s dig into all this support Silverlight provides for working with data.

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An alternative manufacturing process is called outside vapor deposition (OVD). In the OVD process, the soot is deposited on the surface of a rotating ceramic cylinder in two layers. The first layer is the soot that will become the core; the second layer becomes the cladding. Ultimately, the rod and soot are sintered to create a preform. The ceramic is then removed, leaving behind the fused silica that will become the fiber. There are a number of other techniques for creating the preforms that are used to create fiber, but these are the principal techniques in use today. The next step is to convert the preform into optical fiber.

Consultant Services, 516-696-3333) appropriately scaled for neural processing, and shuffled, as required when developing a neural network A series of networks are then trained, beginning with a small network and working up to a fairly large network Most of the networks are simple, 3-layer nets Two 4layer networks are also trained All nets are trained to maximum convergence and then polished to remove any small biases or offsets The process of polishing is achieved by reducing the learning rate to a very small number and then continuing to train the net for about 50 runs Table 1 l-l contains information regarding all networks that were trained for this model, along with the associated correlations and other statistics.

Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website.

To make fiber from a preform, the preform is mounted in a furnace at the top of a tall building called a drawing tower. The bottom of the preform is heated until it has the consistency of taffy, at which time the soft glass is drawn down to form a thin fiber. When it strikes the cooler air outside the furnace, the fiber solidifies. Needless to say, the process is carefully managed to ensure that the thickness of the fiber is precise; microscopes are used to verify the geometry of the fiber. Other stages in the manufacturing process include monitoring processes to check the integrity of the product, a coating process that applies a protective layer, and a take-up stage where the fiber is wound onto reels for later assembly into cables of various types.

In the table, Nei Name = the file name to which the net was saved; Net Size = the number of layers and the number of neurons in each layer; Connecrions = the number of connections in the net optimized by the training process (similar to the number of regression coeff%zients in a multiple regression in terms of their impact on curvefitting and shrinkage); and Correlarion = the multiple correlation of the network output with the target (this is not a squared multiple correlation but an actual correlation) Corrected for Shrinkage covers two columns: The left one represents the correlation corrected for shrinkage under the assumption of an effective sample size of 40,000 data points or facts in the training set The right column represents the correlation corrected for shrinkage under the assumption of 13,000 data points or facts in the training set.

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