User manual THE MATHWORKS NEURAL NETWORK TOOLBOX RELEASE NOTES

[. . . ] Please see www. mathworks. com/patents for more information. Contents Summary by Version . 1 Version 6. 0. 4 (R2010a) Neural Network ToolboxTM Software . 3 Version 6. 0. 3 (R2009b) Neural Network ToolboxTM Software . 4 Version 6. 0. 2 (R2009a) Neural Network ToolboxTM Software . [. . . ] The newpr function creates a pattern recognition network at the command line. Pattern recognition networks are feed-forward networks that solve problems with Boolean or 1-of-N targets and have confusion (plotconfusion) and receiver operating characteristic (plotroc) plots associated with them. The new confusion function calculates the true/false, positive/negative results from comparing network output classification with target classes. New Clustering Training, Initialization, and Plotting GUI The nctool function opens a GUI wizard that guides you to a self-organizing map solution for clustering problems. Users can define their own problem or use one of the new data sets provided. The initsompc function initializes the weights of self-organizing map layers to accelerate training. The learnsomb function implements batch training of SOMs that is orders of magnitude faster than incremental training. The newsom function now creates a SOM network using these faster algorithms. Several new plotting functions are associated with self-organizing maps: · plotsomhits--Plot self-organizing map input hits. · plotsomnc--Plot self-organizing map neighbor connections. 8 Neural Network ToolboxTM Release Notes · plotsomnd--Plot self-organizing map neighbor distances. · plotsomplanes--Plot self-organizing map input weight planes. · plotsompos--Plot self-organizing map weight positions. · plotsomtop--Plot self-organizing map topology. Compatibility Considerations You can call the newsom function using conventions from earlier versions of the toolbox, but using its new calling conventions gives you faster results. New Network Diagram Viewer and Improved Diagram Look The new neural network diagrams support arbitrarily connected network architectures and have an improved layout. Their visual clarity has been improved with color and shading. Network diagrams appear in all the Neural Network Toolbox graphical interfaces. In addition, you can open a network diagram viewer of any network from the command line by typing view(net) New Fitting Network, Plots and Updated Fitting GUI The newfit function creates a fitting network that consistes of a feed-forward backpropagation network with the fitting plot (plotfit) associated with it. The nftool wizard has been updated to use newfit, for simpler operation, to include the new network diagrams, and to include sample data sets. It now allows a Simulink® block version of the trained network to be generated from the final results panel. Compatibility Considerations The code generated by nftool is different the code generated in previous versions. At the command line, the new syntax for using network-creation functions, automates preprocessing, postprocessing, and data-division operations. 11 Version 5. 1 (R2007b) Neural Network ToolboxTM Software For example, the following code returns a network that automatically preprocesses the inputs and targets and postprocesses the outputs: net = newff(p, t, 20); net = train(net, p, t); y = sim(net, p); To create the same network in a previous release, you used the following longer code: [p1, ps1] = removeconstantrows(p); [p2, ps2] = mapminmax(p1); [t1, ts1] = mapminmax(t); pr = minmax(p2); s2 = size(t1, 1); net = newff(pr, [20 s2]); net = train(net, p2, t1); y1 = sim(net, p2) y = mapminmax('reverse', y1, ts1); Default Processing Settings The default input processFcns functions returned with a new network are, as follows: net. inputs{1}. processFcns = . . . {'fixunknowns', 'removeconstantrows', 'mapminmax'} These three processing functions perform the following operations, respectively: · fixunknowns--Encode unknown or missing values (represented by NaN) using numerical values that the network can accept. · removeconstantrows--Remove rows that have constant values across all samples. · mapminmax--Map the minimum and maximum values of each row to the interval [-1 1]. The elements of processParams are set to the default values of the fixunknowns, removeconstantrows, and mapminmax functions. [. . . ] Previously, you had to split the data manually. fixunknowns Encodes Missing Data The fixunknowns function encodes missing values in your data so that they can be processed in a meaningful and consistent way during network training. To reverse this preprocessing operation and return the data to its original state, call fixunknowns again with 'reverse' as the first argument. 20 Neural Network ToolboxTM Release Notes removeconstantrows Handles Constant Values removeconstantrows is a new helper function that processes matrices by removing rows with constant values. mapminmax, mapstd, and processpca Are New The mapminmax, mapstd, and processpca functions are new and perform data preprocessing and postprocessing operations. Compatibility Considerations. Several functions are now obsolete, as described in the following table. New Function mapminmax Obsolete Functions premnmx postmnmx tramnmx prestd poststd trastd prepca trapca mapstd processpca Each new function is more efficient than its obsolete predecessors because it accomplishes both preprocessing and postprocessing of the data. [. . . ]