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This approach is in accord with a suggestion made by Mark Jurik (jurikrescom) After assembling the 18 input variables consisting of the square-root-corrected price differences for a fact, a normalization procedure is applied The intention is to preserve wave shape while discarding amplitude information, By treating the 18 input variables as a vector, the normalization consists of scaling the vector to unit length The calculations involve squaring each vector element or price difference, summing the squares, taking the square-root, and then dividing each element by the resultant number These are the input variables for the neural network In actual fact, the neural network software will further scale these inputs to an appropriate range for the input neurons The target (dependent variable in regression terms) for each fact is simply the time-reversed Slow %K for the current bar The input variables and target for each.



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The ATM Forum looks at service definitions slightly differently than the ITU-T does (see Figure 7-11). Instead of the A-B-C-D services, the ATM





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public void Close() { } public void DeliverMessage(byte[] message, int bytesRead) { } } } As shown in the following code, the Start method of the MessengerServer class listens on port 4530 and invokes the OnConnection method with the callback. Here the IPAddress.Any read-only field provides an IPv4 address that indicates that a server must listen for client activity on all network interfaces for IPv4. The Any field is equivalent to 0.0.0.0 in dotted-decimal notation for IPv4: public void Start() { Listener = new Socket (AddressFamily.InterNetwork, SocketType.Stream, ProtocolType.Tcp); Listener.SetSocketOption(SocketOptionLevel.Tcp, (SocketOptionName)SocketOptionName.NoDelay, 0); // The allowed port range in Silverlight is 4502 to 4534. Listener.Bind(new IPEndPoint(IPAddress.Any, 4530)); // Waiting on connection request Listener.Listen(10); Listener.BeginAccept (new AsyncCallback(OnConnection), null); isRunning = true; } When the MessengerServer receives a connection request, it performs two tasks. First, it creates an instance of a MessengerConnection class to handle the communication. Next, it adds the client to a collection so it can keep track of all the connected clients. This is the only way to achieve interaction between these clients. So the collection here performs the tracking, and we give each new client a different identifying number. The following is a code snippet of the OnConnection method: private void OnConnection(IAsyncResult ar) { if (isRunning==false) return; ClientNo++; // Look for other connections Listener.BeginAccept (new AsyncCallback(OnConnection), null); Console.WriteLine("Messenger client No: " + ClientNo.ToString() + " is connected."); Socket Client = Listener.EndAccept(ar);

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fact are written in simple ASCII format to a file that can be analyzed with a good neural network development package The resultant fact set is used to train a net to predict the time-reversed Slow %K, ie, to predict the relative position of today s close, and, it is hoped, tomorrow s open, with respect to the range of prices over the next 10 bars (a IO-bar timereversed Slow %K) The next step in developing the neural forecaster is to actually train some neural networks using the just-computed fact set A series of neural networks, varying in size, are trained The method used to select the most appropriately sized and trained network is not the usual one of examining behavior on a test set consisting of out-of-sample data.

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Forum categorizes them as real-time and non-real-time services. Under the real-time category, they define constant bit rate services that demand fixed resources with guaranteed availability. They also define real-time, variable bit rate (VBR) service, which provides for statistical multiplexed, variable-bandwidth service allocated on demand. A further subset of real-time VBR is peak-allocated VBR, which guarantees constant loss and delay characteristics for all cells in that flow. Under the non-real-time service class, unspecified bit rate (UBR) is the first service category. UBR is often compared to IP in that it is a best effort delivery scheme in which the network provides whatever bandwidth it has available, with no guarantees made. All recovery functions from lost cells are the responsibility of the end user devices. UBR has two subcategories of its own. The first, non-real-time UBR (NRT-UBR), improves the impacts of cell loss and delay by adding a network resource reservation capability. Available bit rate (ABR), UBR s other subcategory, makes use of feedback information from the far end to manage loss and ensure fair access to and transport across the network. Each of the five classes makes certain guarantees with regard to cell loss, cell delay, and available bandwidth. Furthermore, each of them takes into account descriptors that are characteristic of each service described. These include peak cell rate (PCR), sustained cell rate (SCR), minimum cell rate (MCR), cell delay variation tolerance (CDVT), and burst tolerance (BT).

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