Over 1,000 Windows 95 Programs/Over 1000 Windows 95 Programs (Microforum) (Disc 2).iso/1133/readme.txt

Text File | 1997-04-17 | 19.5 KB | 418 lines

Release Notes NNMODEL Version 1.40

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 modelÆs 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 modelÆs performance statistically - A modelÆs performance
can be evaluated using standard R square statistics.

Display the modelÆs 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.

A data mining utility that allows the user to automatically set up a
historical data matrix, identify variables as factors, responses or
unknown, 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 functions are working in
version 1.27) 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.

DDE Interface - Allows the user to call pre-trained models from
within any program that allows Dynamic Data Exchange. For example, a
user could write an Excel macro to load a BEP model, set the inputs
from the spread sheet, interrogate the modelÆs prediction(s) and
place them back in the spread sheet.

Interrogate External Data Matrices - Data matrices (other than the
training and test matrices) can now be used in the "Model" /
"Interrogate Model" command.  An interrogation DM can be used if it
contains columns for the input and output variables.  The input
variables are loaded into the model and the model is executed, then
the modelÆs predictions are written back into the DM output
variables.

Calculated Columns in Interrogate Model - Neural inputs that were
defined as "Calculated Columns" and based on equations in the
original data matrix can now be automatically calculated and updated.
Previously, the user had to manually calculate these inputs before
the model could predict the outputs.  Caveat - there are four
functions (RUNAVE, LAG, LEAD and DIFLAG) that cannot be automatically
calculated. Models incorporating these functions cannot be
interrogated using the "Interrogate Model" dialog.


NEW FEATURES OF NNMODEL VERSION 1.40 (FEB 97)

New Append Data Matrix - this function was added to facilitate
appending new data records to an existing data matrix. If you find
that you are getting additional data via some electronic source and
it needs to be appended routinely to a 'master' matrix and it's a
pain to get the variable labels into your raw ASCII file then this
function can make life a little easier. To append this data, first
import it into a new data matrix, DO NOT import or edit the variable
labels (use the default labels) then open the master data matrix and
select 'Append Data Matrix' command. If the two matrices have exactly
the same number of columns then the data is quickly added to the end
of the master data matrix.

New Best Model Search Dialog - there has been a new button added to
the search dialog to allow you to edit the neural parameters without
exiting the search routine. The EP Button will invoke the 'Edit
Parameters' dialog to allow you to make any last minute changes to
the neural parameters before starting the search.

New Network Option - Circular Back Propagation options have been
added to the 'Edit Parameters' dialog. What is circular back-prop?
Basically, we've added another 'Theta-like' input to each neuron.
These inputs are fed the sum of the squared values of the network
inputs. CBP can decrease the training time and the network complexity
when modeling some types of processes. Try these options on the VEL
example in the TESTSETS sub-directory.

New Network Option - zero hidden layer neurons. This effectively
removes the hidden layer from the network architecture. If you're
looking for simple linear relationships this can be very fast,
especially if you're using the 'Best Model Search' routine for
discovering unknown relationships in historical data.

New Training Option - keep best model during training. Some times the
best model of a particular process develops somewhere between the
first few seconds of training and the maximum epoch allowed. To
capture this 'best' model can be time consuming and frustrating.
NNMODEL now has options to keep this intermediate model developed
during the training session as the final model. How do we measure
best? NNMODEL allows you to select either the mean square error or R
square as the measurement. You can also select the source of the
measurement as being calculated from the training matrix, the test
matrix or the average of both.

New Training Option - auto save model every 10 minutes during
training session. If the 'Auto Save' Model menu item is checked then
the current state of the model is automatically saved every 10
minutes or every epoch (depending on which is longer).

New Import Function - Replace test matrix. This function allows you
to completely replace the test matrix. However, the importer will
reject any records that are outside of the observed range of the
initial training matrix.

New Import Function - Append training matrix. This function allows
you to append new data to the existing test matrix. As with the
previous function, the importer will reject any records that are
outside of the observed range of the initial training matrix.

New Import Function - Replace training matrix. this function will
allow you to completely replace the training matrix. The importer
will reject any records that are outside of the observed range of the
initial training matrix.

Modified / New Export Functions - Export training or test matrices.
This function was been re-written so that either the training or the
test matrices could be written (in ASCII format) to a file
separately.

New Button - Stop Training. A new tool button has been added to the
toolbar. The button with the X over the train will now stop the
current training session

New Button - CG Tweak. A new tool button has been added to the
toolbar. The button with the 'CG' will run one iteration of the
conjugate gradient weight optimization routine. This may be useful in
training time series data to remove the bias that develops during
back error propagation.

New Graph Options - added standard deviation lines. There has been
three option buttons added to the 'Graph Options' dialog that will
plot either 1, 2 or 3 standard deviation lines on the 'Measured vs.
Predicted' graph, 'Measured and Predicted' or 'Residuals' graphs.

New Graph Option - added linear regression line to the 'Measured vs.
Predicted' graph.


NEW FEATURES OF NNMODEL VERSION 1.30 (NOV 96)

DM - New command "File/Import/Append Test Matrix".  This command lets
you add more data to your test matrix.

DM - New Command "Data/Fill Missing/Interpolate".  The previous "Fill
Missing" command filled the missing data with the last valid value.
With this new command the data can be filled with a linear
interpolated value.

DM - Enhanced Time Lag Function.  A new parameter has been added to
the data variable descriptors.  TimeS can be used to specify that
when building a training matrix this variable should be shifted back
by the number of rows specified.  For example, if each row represents
a 10 minute scan then a TimeS of 12 will cause the training matrix
loaded to include the value 120 minutes in the past from the modeled
output.  When building neural models the outputs will always be set
to zero (in this version).

DM - Logging In Best Model Search.  The model search now logs all
model construction to the file nnmodel.log.  This file is erased when
NNMODEL is first loaded and usually contains only error conditions.
The log can be viewed to see the order that inputs were included into
the best model and various temporary model R squares.

DM - Added Start / Stop functionality to the Best Model Search. This
allows you to stop a search and modify a parameter without having to
re-enter the I/O grid.

NN - New Command "Edit/Remove Inputs".  This command allows you to
remove unnecessary inputs from a neural model.  Many times in data
mining you will add all inputs from a process and build a model then
run a sensitivity analysis on those inputs (to eliminate unneeded
inputs).  Before this command you would have to go back to the
original data matrix and build a new network.  Now you can just
remove the unwanted inputs.  Of course you will still have to re-
train the network.

NN - Sensitivity Report Was Re-written.  The sensitivity report was
completely rewritten.  The sensitivity is calculated by summing the
changes in the output variables caused by moving the input variables
by a small amount over the entire training set.  There are three
variables accumulated during the calculation.  The AbsAve Sensitivity
variable is the average of the absolute values of the change in the
output.  This value is then divided by the total amount of change for
all input variables to normalize the values.  The Ave Sensitivity is
calculated the same as the AbsAve variable except the absolute values
are not taken.  If the direction of the change in the output variable
is always the same then the Ave and AbsAve sensitivities will be
identical.  The third variable calculated is the peak sensitivity and
the row in the training matrix that it occurred.

NN - Additional Information In The Model View.  The internal weights
of the created model are displayed below the standard summary
information.  In addition, this view can now be copied to clipboard
for use word processors.

NN - Simplified Training Graph.  When training a model using the
standard BEP routines (without Automatic Hidden Neuron Addition) the
training graph will show only the normalized sum square error of the
training matrix (black) and the test matrix (red).

NN - Additional Training Method.  A conjugate gradient training
method has been added.  To use this method select "Conjugate
Gradient" as the "Training Method" in the "Edit/Parameters" dialog
screen.  CG training may converge faster on large training matrices.

NN - A new button was added to the "Create Neural Model" dialog. The
button allows you to add variables as both inputs and outputs at the
same time. This can be used for creating autoassociative networks
that predict the inputs from themselves. This is the first step in
creating a sensor validation network.


BUGS FIXED IN VERSION 1.40 (FEB 97)

'Bad Memory Pointer' while running basic statistics report. This is
caused by a memory overwrite during the formatting of the statistics.
It is very data dependent and can only be caused when very large
numbers are present in the data.

Floating point error loading sparse matrix with a design type of
Star-Simplex. This is caused by a bug that allows more then the
needed number of rows to be loaded. The floating point error is
generated when the grid tries to display data that isn't really
there.
Correlation report causes floating point error with very large data
matrices. This bug was discovered when a correlation report was
generated on a 14,000 rows by 65 columns data matrix. A floating
point overflow error was generated during the calculation of F
statistic when F-stat exceeded the dynamic range. The error was data
dependent and had nothing to do with the size of the matrix, but
rather the content of the data. The routine that had the bug is used
in the correlation report and scatter plot routines.

Create design data matrix failed This bug was introduced in version
1.303 due to a programming bug. It prevents you creating any type of
designed matrix.

Min/Max values not copied from data matrix The minimum and maximum
values were always re-calculated from the data rather than the
desired min/max values.

Best model search start/stop button After search terminates the
button still reads stop and then when you click on it it reads start
but does not start anything.

Remove Inputs corrupts data matrix. There is a problem with this
function, where entire columns of data may be corrupted and the
incorrect input may be removed.  The symptom is a constant Rsq of 0
for your test matrix.


BUGS FIXED IN VERSION 1.30 (NOV 96)

The 187 Column Bug has finally been fixed.  The problem stemmed from
a vendor supplied grid library.  This library was replaced in the
"Import Raw Data" dialog with another vendorÆs grid.  This
necessitated the adding of yet another DLL file to the project
directory.  In version 1.30 of NNMODEL the number of columns that can
be created has been raised to 1024.

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 æVÆ 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.

Best Model Search - floating point overflow

Best Model Search - using test matrix rsq no models could be found

Import Test Matrix doesnÆt load correctly or gives a protection
error.

Correlation report causes divide by zero error.

A few bugs were found in the NNLIB source code in deallocating memory

Sparse Data Loader - a bug was fixed that caused no data to be loaded
if any columns were skipped in the data matrix.


NEW FEATURES OF NNCALC VERSION 1.3

NNCalc has been modified to support Circular Back-Propagation. To get
an updated version of the professional edition contact
[e-mail] to get the update e-mailed to you.


NEW FEATURES OF NNCALC VERSION 1.2

Because NNCalc only returns the first output of a neural model (a
limitation of Excel) a function was needed to get the additional
model outputs.  NNCalcM returns the predicted values for models that
have more than one output.  NNCalcM does not evaluate the model
(thatÆs NNCalcs job).  It simply returns the networkÆs output value.

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

To install NNMODEL from FLOPPIES:

    1) Insert disk 1
    2) From Window's program manager select File / Run and type:
        A:\SETUP.EXE

To install NNMODEL from a ZIP archive:

    1) Copy the archive to a temporary directory and unzip (i.e. C:\TMP)
    2) 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 have any further questions, problems or program bugs please email
them to [e-mail] or visit our home page at

    www.neuralfusion.com