
 C. Menus
  1. Using Menus
   a. Use mouse or arrow keys to move cursor. Press "enter" to make
      a choice, or click the mouse.

  2.Menu Options
     I.Neural Network
     a.Utilities and tools:
       Selecting Folder to store data,view ASCII files,combine files,
       split a file,and examine a file ( calculate means, standard 
       deviations,plot columns and their histograms respectively.
     c.Trouble shooting
       List some problem the user will encounter and the solutions.

    II.Data Pre-Processing:
       Create Time Series Training Data: Create a training data file
       from a file of columnar time series data.
       Data Compression: Compress or expand training data file inputs
       or outputs using KLT matrix.
       Feature selection: Select some inputs based on the importance of
       the inputs.
       
   III.MLP Nets
       MLP Sizing from Data: Estimate size of MLP from training data file
       Fast Training: Design MLP neural nets via fast training
       Pruning a Trained Net: Analyze and Prune a trained MLP
       Automated MLP Design: Design an MLP with little user input
       Process Data Using a Trained Net: Process a data file using a neural net
       
   IV.Other Neural Nets
       a.Functional Link Nets:
         Training:train the data using functional link nets for mapping
         Testing: use the trainied weights to test the data using functional link nets
         Processing:process the tested data and check for desired output.
       b.Piecewise linear nets:
         Training:train the data using piecewise linear net for mapping
         Testing: use the trainied weights to test the data using piecewise linear net
         Processing:process the tested data and check for desired output.   
   
       C.Unsupervised Learning
         Cluster data file using
         Kmean: Using K-Means to classify the training data.
         SOM: Neural Clustering (Kohonen's Self-Organizing Map) to classify the training data
         Classify vectors:classify the input training vectors using the cluster centers
         using a very simple nearest neighbor classifier.
     V.Help
        a.System requirements: Required main memory and disk space, etc.
        b.Terminology: Definitions of technical terms used in neural nets
          and in this package
        c.File formats: File formats for network structure, weights, training
          data, testing data, and network outputs
        d.Main menu choices: current file
        e.Getting started: Information on how to use this package.
        f.Frequently asked questions: Questions that people have asked or should
          ask about this software package, along with the answers
        g.View manual: View the manual, which is a document composed of the
          help files
