NeuroSolutions for Excel

Category Intelligent Software>Neural Network Systems/Tools

Abstract NeuroSolutions for Excel is a Microsoft Excel add-in that simplifies and enhances the process of getting data into and out of a NeuroSolutions (see G6G Abstract Number 20036) neural network. This tool benefits both the novice and the advanced neural network developer by offering easy to use, yet extremely powerful features. The foremost feature of this product is that all tasks can be performed directly from Excel!

NeuroSolutions for Excel is organized into seven (7) modules, each of which can be extended with user-defined custom batches written in Visual Basic for Applications.

Preprocess Data Module - The Preprocess Data module allows the user to apply various preprocessing techniques to their raw data to prepare it for input into a neural network. The following Preprocess Data operations are built into NeuroSolutions for Excel:

1) Difference between Columns; 2) Insert Column Labels; 3) Sub- sample Rows; 4) Moving Average of Column;

5) Shift - The input data is adjusted to either move the inputs back by a specified shift value to do predictions or move the inputs forward to lead your desired output; 6) Encode Two Class Column - The selected column of data is checked to verify that there are two classes contained within the column and is then encoded into another column. The data to be encoded can be textual or numeric, but must be translated to only numeric, integer codes. The encoded column will be written in the first empty column in the dataset;

7) Pause Training - As an alternative to completely stopping a training run, the network can be paused, so training can be resumed at the same point; 8) Classification Report - A classification report is generated if the desired output of the testing set is a single two-class column; 9) Randomize Rows - Randomly arranges the rows of data within the active worksheet. This is performed on ordered data so that the training data is representative of the entire data set; 10) Translate Symbolic Columns - Translates textual columns into columns of 0's and 1's. This is a requirement for columns containing words since neural networks can only work with numeric data; 11) Clean Data - Cleans the data by replacing blank cells, error codes, and/or user- defined text with an interpolated value, the column average, a random value, or the closest value in a column.

Analyze Data Module - The Analyze Data module provides the user with useful information about their data. The operations available in this module can be used during the preprocessing stage of neural network design or to analyze the network output. The following Analyze Data operations are built into NeuroSolutions for Excel:

1) Histogram; 2) Time Series Plot; 3) X-Y Scatter Plot; 4) Correlation - Computes the correlation between each of the columns of data and generates a table; 5) Summary Statistics - Computes several key statistics for the selected column(s) of data, such as Mean, Standard Error (of the mean), Median, Mode, Standard Deviation, Variance, Kurtosis, Skewness, Range, Minimum, Maximum, Sum, Count, Mean Confidence Level, Kth Largest Value and Kth Smallest Value; 6) Trend Accuracy - The trend accuracy measure is useful when working with time series data. It gives the percentage for which the actual output changed in the correct direction relative to the previous desired value.

Tag Data Module - The Tag Data module provides a simple graphical method for tagging portions of your data as Training Input, Training Desired, Cross Validation Input, Cross Validation Desired, Testing Input, Testing Desired, and Production Input. This module also provides powerful auto-tag methods. The following Tag Data operations are built into NeuroSolutions for Excel:

1) Tag Selected Column(s) As Input, Desired or Symbol; 2) Tag Selected Row(s) As Training, Cross Validation, Testing or Production; 3) Clear Selected Tags; 4) Tag Rows By Percentages - Tags the rows of data within the active worksheet as Training, Cross Validation, and Testing according to user-defined percentages; 5) Select Cross- section - Allows you to automatically select a cross-section of data based on how it is tagged.

Create/Open Network Module - The Create/Open Network module allows the user to create a NeuroSolutions breadboard (an experimental neural network) from scratch through the use of the NeuralBuilder utility or by opening an existing NeuroSolutions breadboard. The following Create/Open Network operations are built into NeuroSolutions for Excel:

1) Open, Close or Save Breadboard; 2) New Classification Network - Automatically creates a breadboard suited for Classification problems; 3) New Function Approximation Network - Automatically creates a breadboard suited for Function Approximation problems; 4) New Custom Network - Starts the NeuralBuilder which guides the user through the creation of a new NeuroSolutions breadboard; 5) Load Best Weights - Loads the best weights for the active breadboard.

Create Data Files Module - The Create Data Files module creates tab- delimited American Standard Code for Information Interchange (ASCII) files for each tagged cross-section so that NeuroSolutions can read the data. This module is Not used very often since the files are created automatically whenever the neural network is trained or tested. The following Create Data Files operations are built into NeuroSolutions for Excel:

1) Create All Data Files; 2) Create Files for one Data Set (Training, Cross Validation, Testing or Production); 3) View Data Files; 4) Delete All Data Files.

Train Network Module - The Train Network module gives the user the ability to train a network once, multiple times with different random initial conditions, or multiple times while varying one or more network parameters. This module permits the user to find the optimum network for a particular problem. The following Train Network operations are built into NeuroSolutions for Excel:

1) Train - Trains the active NeuroSolutions breadboard one time and creates a report of the results; 2) Train N Times - Trains the active NeuroSolutions breadboard N times with different random initial conditions and creates a report of the results; 3) Leave N Out Training - Trains the network multiple times, each time omitting a different subset of the data and using that subset for testing. The outputs from each tested subset are combined into one testing report and the model is trained one additional time using all of the data; 4) Vary a Parameter - Trains the active NeuroSolutions breadboard N times for each value of a network parameter as the parameter is varied from a user defined starting value by a user defined increment for a user defined number of variations; 5) Train Genetic - Trains the active NeuroSolutions breadboard while genetically optimizing the choice of inputs and parameter values to achieve the best model.

Test Network Module - The Test Network module can be used to test a network after training has been completed. In testing the network, various performance measures are computed. This module also allows you to perform sensitivity analysis on the network. The following Test Network operations are built into NeuroSolutions for Excel:

1) Test - Tests the active NeuroSolutions breadboard on the chosen data set and creates a report of the results; 2) Sensitivity About the Mean - Performs sensitivity analysis on the chosen data set. This procedure allows you to determine the effect each of the inputs has on the network output.

Custom Batches - The user can also create his/her own custom subroutines (batches) for any of the seven (above) modules by calling built-in NeuroSolutions functions and/or writing Visual Basic code. These custom batches can then be run from the NeuroSolutions for Excel menu from within Microsoft Excel.

System Requirements

Windows 98/ME/2000/XP/Vista; Excel 97 / 2000 / XP / 2003 / 2007; NeuroSolutions v5.XX.

Manufacturer

Manufacturer Web Site NeuroDimension, Inc.

Price $295 (not a stand-alone product; requires license of NeuroSolutions)

G6G Abstract Number 20038

G6G Manufacturer Number 101916