PC World Komputer 1997 February/PCWK0297.iso/technika/nnmodel/readme.txt

Text File | 1996-05-13 | 9KB | 183 lines

                        What is NNMODEL

NNMODEL is a cost effective way of modeling process data, statistical experiments,
or historical databases. It can find from simple linear to complex non-linear
relationships in empirical data. It is easy to use because it automatically
constructs mathematical models directly from your data. It enables you to create
prototype models quickly and inexpensively.

NNMODEL is designed to help you get maximum benefit from powerful neural network
modeling techniques without requiring you to learn a complicated software package
or statistical language. Thus, you can learn how to use NNMODEL and start solving
real world problems within a few hours.

NNMODEL currently contains program modules to:

    Design a statistical experiment - NNMODEL allows you to create a data
    matrix based on a statistically designed experiment. A designed data matrix
    will allow you to squeeze the most information from a finite number of
    observations. The types of designs available are: two level, three level,
    simplex, star-simplex, central composite and multilevel.

     Keyboard enter, file or clipboard import the data - There are three methods
    for entering data into NNMODEL: 1) Enter the data directly using the built in
    data matrix editor, 2) import an ASCII tab or blank delimited file or 3) paste
    data from the Windows clipboard.

     Run simple statistics and correlation reports - You can generate a report that
    contains the basic statistics, such as, number of observations, maximum,
    minimum, average, standard deviation and sum of squares. Or generate a
    correlation report contains the results Pearson Correlation Coefficients,
    Probability > |R| under Ho and Rho:=0 / N.

     Graphically analyze the raw data - You can view the data graphically using a
    variety of plotting routines including: trend plot by observation, XY scatter,
    frequency distribution, 3 dimensional scatter. Thumbnail views of all the data
    can be printed for the trend, scatter and distribution plots.

     Load historical data into a designed experiment matrix - A designed data
    matrix can be created as an empty shell and later loaded by the historical
    data loader. This imposes a designed experiment onto the historical data to
    better insure any resulting model's long term success. This method also has
    two side benefits, you get to see how much of the design space is really
    represented in the data and it generates a smaller training matrix so the
    training step proceeds faster.

     Advice on missing observations - After historical data has been loaded into a
    designed experiment the Missing Advisor can be used to suggest trials or
    treatments to run that would balance the design space. Thus, extracting
    more information from the data.

     Add equations or calculated columns to the data matrix - Columns of data
    can be created by defining an equation based on the other columns. A
    simple equation parser is built into the data matrix editor. Rows of data can
    be excluded from reports, graphs or models by using an exclude equation.

     Model the data using neural networks - The whole purpose of NNMODEL is
    to build neural models. A model can be created and trained in just a few
    minutes.

     Interrogate the model interactively - After a model has been trained you can
    immediately ask the model to predict using combination of input levels not
    seen in the data.

     Analyze the model's performance statistically - A modelÆs performance can
    be evaluated using standard R square statistics.

     Display the model's predictions graphically including 3D and contour plots -
    A number of graphs are available for validating a model including: measured
    vs. predicted, measured overlaid on predicted, residual plots, trends, scatter
    plots, frequency distributions, XY plots, 3D surface maps and contour plots.

     Test the model on additional external data sets - a test matrix can be loaded
    from data matrices not originally used to generate the model. This type of
    testing may be the only way of validating models generated from
    undesigned data.

     Perform sensitivity analysis - This analysis can show you how sensitive an
    output variable is to changes made to the inputs. The results are ranked in
    order with the variables with the most effect at the top of the list.

     Export the neural model as a transportable ASCII file - Trained models can
    be exported from NNMODEL to any other hardware platform. Neural models
    can be included with user software by linking with the NNLIB library.

Planned add-ons to NNMODEL:

     Multi-Module Optimizer  Combine one or more neural models with algebraic
    equations to minimize or maximize any combination of inputs, outputs or
    cost functions. The optimizer utilizes a Monte Carlo started constrained
    conjugate gradient algorithm to minimize the objective function. The
    objective function can be constructed from any or all inputs or outputs along
    with their polarity (min or max) and their relative weight. Inputs can be
    constrained rectilinearly, outputs are constrained by a penalty function.
    Results of the optimizations can be viewed using the interactive
    interrogation module, graphically or by viewing results log.

     Multi-Module Simulator  Combine one or more neural models with
    interpreted algebraic equations or pre-compiled user subroutines (user
    creates a DLL file). Simulator is an OLE container that can link with many
    graphical display modules and VBX controls. The simulator is designed
    using the source/sink concept. Data sources are ASCII files, OLE or DDE
    modules, models or equations. Sinks are reports, graphs, meters, equations
    or models.

     Attribute Data  Automatic conversion of Attribute data to a continuous
    variable based on a user defined rank or conversion to discrete logical
    variables (1 or 0). The continuous variable simply becomes one input to the
    model. However, the discrete variable creates as many inputs as there are
    states.

     Real Time Data Matrix Loader  Using the DDE interface automatically load a
    designed data matrix. The data matrix can be exported to be used to build a
    neural model. The neural model can then be used to control the process
    monitored by the DDE source.

     OODB Linkage  Allow the data matrix to be created directly from an open
    OODB database such as Microsoft Access.


The following bugs have been fixed:

    Loading large files causes Windows error. There is a bug in the data matrix
    loader that causes an application error while loading files with more than
    16000 records.

    Export data matrix as ASCII. There is a missing carriage return and linefeed
    after the UNITS line in th raw file.

    Import string causes heap error. The maximum field size for a number/string
    is 20 characters. If this is exceeded a memory overrun error is generated.
    To fix this problem shorten all fields to less than 20 characters.

    Test data records are not appearing in neural model test matrix when editing
    a 'V' into the RT field. To fix this problem press the "ReCalc" button on the
    toolbar before creating the model.

       Thumbnail graphs can only be printed starting at page 1.

    Forgot to include header files for NNLIB.


The following new features have been added:

     Data Mining Utility  Allows the user to automatically set up a historical data
    matrix, identify variables as factors, responses or unknown, time position (up
    or down stream) in time units, use full dataset for modeling or select records
    from the database based on goodness of fit to a multi-level design, pick the
    best factors for inclusion into the model based on model performance,
    include or exclude factors for any model based on prior knowledge, report
    results of search. (NOTE: Not all function are working in version 1.21) To use
    select "Data / Best Model Search".

    Train neural network from very large data matrix. The version allows an external
    binary file to be used as the training matrix. To use build the binary file
    using the "Import Raw File" with the "Create Binary File" radio button checked.
    The file can then be used during training by checking the "Model / Use Ext
    Binary File" menu item.

***********************************************************************

This ZIP file contains the files necessary to install NNMODEL.  Copy
nnmod121.zip to a temporary directory (C:\TMP) and unzip the archive
by typing:

CD C:\TMP
PKUNZIP NNMOD121

Then from Window's program manager select File / Run and type:

C:\TMP\SETUP.EXE

The SETUP program will install NNMODEL onto your system.

If you distribute NNMODEL to friends, associates,  or to a computer bulletin
board system (BBS), please distribute the file NNMOD121.ZIP rather than the
individual files.

If you have any further questions, problems or program bugs please e-mail
them to [e-mail] or visit our home page at

http://www.wvtc.com/~carlb/