You may also prune the weights that exceed a threshold that you set. The weights are displayed with more precision than they were in previous releases of NeuroShell 2. When training a network, you have a choice of viewing the weights of either the best network or the last network. However, we still believe that the best factors are generated by PNN and GRNN genetic adaptive nets. We believe the contribution factors generated by this new method are more reliable. Release 3 adds a more sophisticated method of measuring contribution factors than we had in the past. The option to specify whether to place the training and/or test set(s) in memory has been removed from the Training and Stop Training Criteria module. Release 3 automatically loads training and test files into memory. Networks created in previous releases may be run in Release 3, however. You cannot run Release 3 networks in previous releases. FIG files from Release 3 are not compatible with previous releases. The change was made because the Ward Net generally gives better results than a simple three layer backpropagation network. The Architectures Module defaults to the second Ward Network rather than a simple three layer backpropagation network.
In Windows 3.1, this choice is made by opening the Control Panel, and International icons and selecting Number. In Windows 95, this choice is made in the Control Panel, Regional Settings, Number option. Release 3 honors your Windows selection of either a comma or a period for a decimal separator. International Decimal Separator and List Separator Refer to DLL Server for more information on using the Predict function. Refer to International Decimal Separator and List Separator described below for details. The Predict function uses the list separator (either a comma or period) that you specified in Windows. Note: If you intend to execute a trained network on a computer other than the one used to train the network, you must determine whether the computer which will run the network will be using either Excel 7 or Excel 5 for NT and supply the correct. It is the 32-bit version of the 16-bit NSHELL2.DLL.Ĭalls in Excel 7 are case sensitive, so you must make sure the upper and lower case letters match the following: NS2-32.DLL is installed in the \WINDOWS\SYSTEM directory during NeuroShell 2 setup.
If you want to use the OpenNet, FireNet, CloseNet, or Predict functions with either Excel 7 or Excel 5 for NT, you need to substitute the file called NS2-32.DLL for NSHELL2.DLL in your call statement (refer to DLL Server for more information on how to use these functions). Finally, the extraction percentage is more accurate than it was before.Ĭalling the Predict Function from Excel 7 or Excel 5 for NT The release also changes the default percent extraction for a test set to 20 percent from 10 percent in previous releases. Release 3 adds an additional extraction method to the Test Set Extract module which allows you to select a random test set and at the same time select a production file from the end of the. Calibration more accurately reflects the concept of NET-PERFECT, which optimizes the network by applying the current network to an independent test set during training. The term Calibration replaces NET-PERFECT in the NeuroShell 2 screens and help files. An Omega Downloader file is a data file created by Wall Street Analyst, TradeStation, or SuperCharts programs from Omega Research, Inc. This module imports an Omega Downloader data file and converts it to NeuroShell 2 internal file format. If you are running Windows 3.1, you can train your networks only in 16-bit mode. If you are using 32-bit mode, training speed varies depending upon whether you are using Windows 95 or Windows NT. If you are using either Windows 95 or Windows NT, you may choose to train your networks in either 16 or 32-bit mode.
For more information, refer to Source Code Generator.
The Source Code Generator is found in the Runtime module. If you have large nets, the source code may have to be broken into several pieces so that it will compile. Once you have trained a network using NeuroShell 2, you can generate Visual Basic or C source code which you can compile into other programs. For more information, refer to GMDH Architecture. The architecture computes a mathematical formula which is a nonlinear polynomial expression relating the values of the most important inputs to predict the output variable. The new release adds a new network architecture, GMDH (Group Method of Data Handling) or polynomial nets. This section will detail changes between NeuroShell 2 Release 3.0 and Release 2.0.