Neural Network Toolbox™
Release Notes
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Neural Network Toolbox™ Release Notes
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Contents 1
Summary by Version . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Version 6.0.4 (R2010a) Neural Network Toolbox™ Software . 3
Version 6.0.3 (R2009b) Neural Network Toolbox™ Software . 4
Version 6.0.2 (R2009a) Neural Network Toolbox™ Software . 5
Version 6.0.1 (R2008b) Neural Network Toolbox™ Software . 6
Version 6.0 (R2008a) Neural Network Toolbox™ Software . . 7
Version 5.1 (R2007b) Neural Network Toolbox™ Software . 10
Version 5.0.2 (R2007a) Neural Network Toolbox™
Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17
Version 5.0.1 (R2006b) Neural Network Toolbox™
Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Version 5.0 (R2006a) Neural Network Toolbox™ Software . 19
Version 4.0.6 (R14SP3) Neural Network Toolbox™
Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Compatibility Summary for Neural Network Toolbox™
Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18
23
24
Neural Network Toolbox Release Notes
iv
Summary by Version
This table provides quick access to what’s new in each version. For
clarification, see Using Release Notes.
Neural Network Toolbox™ Release Notes
Versi o n
(Release)
Latest Version
V6.0.4 (R2010a)
New Features
and Changes
Versi o n
Compatibility
Considerations
Fixed Bugs and
Known Problems
No No Bug Reports
Includes fixes
Related
Documentation at
Web Site
Printable Release
Notes: PDF
Current product
documentation
V6.0.3 (R2009b)
No No Bug Reports
None
Includes fixes
V6.0.2 (R2009a)
No No Bug Reports
None
Includes fixes
V6.0.1 (R2008b)
No No Bug Reports
None
Includes fixes
V6.0 (R2008a)
V5.1 (R2007b)
Yes
Details
Yes
Details
Yes
Summary
Yes
Summary
Bug Reports
Includes fixes
Bug Reports
Includes fixes
None
None
V5.0.2 (R2007a) No No Bug Reports None
V5.0.1 (R2006b) No No Bug Reports None
V5.0 (R2006a) Yes
Details
Yes
Summary
Bug Reports None
V4.0.6 (R14SP3) No No Bug Reports None
Using Release Notes
Use release notes when upgrading to a newer version to learn about:
1
Summary by Version
• New features
• Changes
• Potential impact on your existing files and practices
Review the release notes for other MathWorks™ products required for this
product (for example, MATLAB
compatibility considerations that also might impact you.
If you are upgrading from a software version other than the most recent one,
review the release notes for all interim versions, not just for the version you are
installing. For example, when upgrading from V1.0 to V1.2, review the release
notes for V1.1 and V1.2.
®
or Simulink®) for enhancements, bugs, and
What’s in the Release Notes
New Features and Changes
• New functionality
• Changes to existing functionality
Version Compatibility Considerations
When a new feature or change introduces a reported incompatibility between
versions, the
Compatibility issues reported after the product is released appear under Bug
Reports at the MathWorks Web site. Bug fixes can sometimes result in
incompatibilities, so you should also review fixed bugs in Bug Reports for any
compatibility impact.
Compatibility Considerations subsection explains the impact.
Fixed Bugs and Known Problems
The MathWorks offers a user-searchable database so you can view Bug
Reports. The development team updates this database at release time and as
more information becomes available. This includes provisions for any known
workarounds or file replacements. Information is available for bugs existing in
or fixed in Release 14SP2 or later. Information is not available for all bugs in
earlier releases.
Access Bug Reports using your MathWorks Account.
2
Neural Network Toolbox™ Release Notes
Version 6.0.4 (R2010a) Neural Network Toolbox™ Software
This table summarizes what’s new in Version 6.0.4 (R2010a).
New Features
and Changes
No No Bug Reports
Version
Compatibility
Considerations
There are no new features or changes in this version.
Fixed Bugs and
Known Problems
Includes fixes
Related Documentation at
Web Site
Printable Release Notes: PDF
Current product
documentation
3
Version 6.0.3 (R2009b) Neural Network Toolbox™ Software
Version 6.0.3 (R2009b) Neural Network Toolbox™
Software
This table summarizes what’s new in Version 6.0.3 (R2009b).
New Features
and Changes
No No Bug Reports
Version
Compatibility
Considerations
There are no new features or changes in this version.
Fixed Bugs and
Known Problems
Includes fixes
Related Documentation at
Web Site
None
4
Neural Network Toolbox™ Release Notes
Version 6.0.2 (R2009a) Neural Network Toolbox™ Software
This table summarizes what’s new in Version 6.0.2 (R2009a).
New Features
and Changes
No No Bug Reports
Version
Compatibility
Considerations
There are no new features or changes in this version.
Fixed Bugs and
Known Problems
Includes fixes
Related Documentation at
Web Site
None
5
Version 6.0.1 (R2008b) Neural Network Toolbox™ Software
Version 6.0.1 (R2008b) Neural Network Toolbox™
Software
This table summarizes what’s new in Version 6.0.1 (R2008b).
New Features
and Changes
No No Bug Reports
Version
Compatibility
Considerations
There are no new features or changes in this version.
Fixed Bugs and
Known Problems
Includes fixes
Related Documentation at
Web Site
None
6
Neural Network Toolbox™ Release Notes
Version 6.0 (R2008a) Neural Network Toolbox™ Software
This table summarizes what’s new in Version 6.0 (R2008a):
New Features
and Changes
Yes
Details below
Version
Compatibility
Considerations
Yes—Details
labeled as
Compatibility
Considerations
Fixed Bugs and
Known Problems
Bug Reports
Includes fixes
,
Related Documentation at
Web Site
None
below. See also
Summary.
New features and changes introduced in this version are:
• “New Training GUI with Animated Plotting Functions” on page 7
• “New Pattern Recognition Network, Plotting, and Analysis GUI” on page 8
• “New Clustering Training, Initialization, and Plotting GUI” on page 8
• “New Network Diagram Viewer and Improved Diagram Look” on page 9
• “New Fitting Network, Plots and Updated Fitting GUI” on page 9
New Training GUI with Animated Plotting Functions
Training networks with the train function now automatically opens a window
that shows the network diagram, training algorithm names, and training
status information.
The window also includes buttons for plots associated with the network being
trained. These buttons launch the plots during or after training. If the plots are
open during training, they update every epoch, resulting in animations that
make understanding network performance much easier.
The training window can be opened and closed at the command line as follows:
nntraintool
nntraintool('close')
Two plotting functions associated with the most networks are:
7
Version 6.0 (R2008a) Neural Network Toolbox™ Software
• plotperform—Plot performance.
•
plottrainstate—Plot training state.
Compatibility Considerations
To turn off the new training window and display command-line output (which
was the default display in previous versions), use these two training
parameters:
net.trainParam.showWindow = false;
net.trainParam.showCommandLine = true;
New Pattern Recognition Network, Plotting, and
Analysis GUI
The nprtool function opens a GUI wizard that guides you to a neural network
solution for pattern recognition problems. Users can define their own problems
or use one of the new data sets provided.
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-ofreceiver operating characteristic (
N targets and have confusion (plotconfusion) and
plotroc) plots associated with them.
The new
from comparing network output classification with target classes.
confusion function calculates the true/false, positive/negative results
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
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
learnsomb function implements batch training of
Neural Network Toolbox™ 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 (
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
the final results panel.
®
block version of the trained network to be generated from
Compatibility Considerations
The code generated by nftool is different the code generated in previous
versions. However, the code generated by earlier versions still operates
correctly.
plotfit) associated with it.
9
Version 5.1 (R2007b) Neural Network Toolbox™ Software
Version 5.1 (R2007b) Neural Network Toolbox™ Software
This table summarizes what’s new in Version 5.1 (R2007b):
New Features
and Changes
Yes
Details below
Version
Compatibility
Considerations
Yes—Details
labeled as
Compatibility
Considerations
Fixed Bugs and
Known Problems
Bug Reports
Includes fixes
,
Related Documentation at
Web Site
None
below. See also
Summary.
New features and changes introduced in this version are:
• “Simplified Syntax for Network-Creation Functions” on page 10
• “Automated Data Preprocessing and Postprocessing During Network
Creation” on page 11
• “Automated Data Division During Network Creation” on page 14
• “New Simulink Blocks for Data Preprocessing” on page 16
• “Properties for Targets Now Defined by Properties for Outputs” on page 16
Simplified Syntax for Network-Creation Functions
The following network-creation functions have new input arguments to
simplify the network creation process:
10
newcf
•
• newff
• newdtdnn
• newelm
• newfftd
• newlin
• newlrn
• newnarx
• newnarxsp
Neural Network Toolbox™ Release Notes
For detailed information about each function, see the corresponding reference
pages.
Changes to the syntax of network-creation functions have the following
benefits:
• You can now specify input and target data values directly. In the previous
release, you specified input ranges and the size of the output layer instead.
• The new syntax automates preprocessing, data division, and postprocessing
of data.
For example, to create a two-layer feed-forward network with 20 neurons in its
hidden layer for a given a matrix of input vectors
can now use
net = newff(p,t,20);
This command also sets properties of the network such that the functions sim
and
train automatically preprocess inputs and targets, and postprocess
outputs.
In the previous release, you had to use the following three commands to create
the same network:
newff with the following arguments:
p and target vectors t, you
pr = minmax(p);
s2 = size(t,1);
net = newff(pr,[20 s2]);
Compatibility Considerations
Your existing code still works but might produce a warning that you are using
obsolete syntax.
Automated Data Preprocessing and Postprocessing
During Network Creation
Automated data preprocessing and postprocessing occur during network
creation in the Network/Data Manager GUI, Neural Network Fitting Tool GUI,
and at the command line.
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 Toolbox™ 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:
12
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
The elements of
fixunknowns, removeconstantrows, and mapminmax functions.
The default output
[-1 1].
processParams are set to the default values of the
processFcns functions returned with a new network
include the following:
Neural Network Toolbox™ Release Notes
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'}
These defaults process outputs by removing rows with constant values across
all samples and mapping the values to the interval
sim and train automatically process inputs and targets using the input and
output processing functions, respectively.
sim and train also reverse-process
[-1 1].
network outputs as specified by the output processing functions.
For more information about processing input, target, and output data, see
“Backpropagation” in the Neural Network Toolbox™ User’s Guide
documentation.
Changing Default Input Processing Functions
You can change the default processing functions either by specifying optional
processing function arguments with the network-creation function, or by
changing the value of
You can also modify the default parameters for each processing function by
changing the elements of the
processFcns after creating your network.
processParams properties.
After you create a network object (
net), you can use the following input
properties to view and modify the automatic processing settings:
net.inputs{1}.exampleInput—Matrix of example input vectors
•
•
net.inputs{1}.processFcns—Cell array of processing function names
•
net.inputs{1}.processParams—Cell array of processing parameters
The following input properties are automatically set and you cannot change
them:
•
net.inputs{1}.processSettings—Cell array of processing settings
•
net.inputs{1}.processedRange—Ranges of example input vectors after
processing
•
net.inputs{1}.processedSize—Number of input elements after
processing
Changing Default Output Processing Functions
After you create a network object (net), you can use the following output
properties to view and modify the automatic processing settings:
13
Version 5.1 (R2007b) Neural Network Toolbox™ Software
• net.outputs{2}.exampleOutput—Matrix of example output vectors
•
net.outputs{2}.processFcns—Cell array of processing function names
•
net.outputs{2}.processParams—Cell array of processing parameters
Note These output properties require a network that has the output layer as
the second layer.
The following new output properties are automatically set and you cannot
change them:
•
net.outputs{2}.processSettings—Cell array of processing settings
•
net.outputs{2}.processedRange—Ranges of example output vectors after
processing
•
net.outputs{2}.processedSize—Number of input elements after
processing
14
Automated Data Division During Network Creation
When training with supervised training functions, such as the
Levenberg-Marquardt backpropagation (the default for feed-forward
networks), you can supply three sets of input and target data. The first data set
trains the network, the second data set stops training when generalization
begins to suffer, and the third data set provides an independent measure of
network performance.
Automated data division occurs during network creation in the Network/Data
Manager GUI, Neural Network Fitting Tool GUI, and at the command line.
At the command line, to create and train a network with early stopping that
uses 20% of samples for validation and 20% for testing, you can use the
following code:
net = newff(p,t,20);
net = train(net,p,t);
Previously, you entered the following code to accomplish the same result:
pr = minmax(p);
s2 = size(t,1);
Neural Network Toolbox™ Release Notes
net = newff(pr,[20 s2]);
[trainV,validateV,testV] = dividevec(p,t,0.2,0.2);
[net,tr] = train(net,trainV.P,trainV.T,[],[],validateV,testV);
For more information about data division, see “Backpropagation” in the Neural
Network Toolbox™ User’s Guide documentation.
New Data Division Functions
The following are new data division functions:
•
dividerand—Divide vectors using random indices.
•
divideblock—Divide vectors in three blocks of indices.
•
divideint—Divide vectors with interleaved indices.
•
divideind—Divide vectors according to supplied indices.
Default Data Division Settings
Network creation functions return the following default data division
properties:
net.divideFcn = 'dividerand'
•
• net.divedeParam.trainRatio = 0.6;
• net.divideParam.valRatio = 0.2;
• net.divideParam.testRatio = 0.2;
Calling train on the network object net divided the set of input and target
vectors into three sets, such that 60% of the vectors are used for training, 20%
for validation, and 20% for independent testing.
Changing Default Data Division Settings
You can override default data division settings by either supplying the optional
data division argument for a network-creation function, or by changing the
corresponding property values after creating the network.
After creating a network, you can view and modify the data division behavior
using the following new network properties:
•
net.divideFcn - Name of the division function
•
net.divideParam - Parameters for the division function
15
Version 5.1 (R2007b) Neural Network Toolbox™ Software
New Simulink Blocks for Data Preprocessing
New blocks for data processing and reverse processing are available. For more
information, see the description of processing blocks.
The function
new processing blocks.
gensim now generates neural networks in Simulink
Properties for Targets Now Defined by Properties
for Outputs
The properties for targets are now defined by the properties for outputs. Use
the following properties to get and set the output and target properties of your
network:
•
net.numOutputs—The number of outputs and targets
•
net.outputConnect—Indicates which layers have outputs and targets
•
net.outputs—Cell array of output subobjects defining each output and its
target
®
that use the
16
Compatibility Considerations
Several properties are now obsolete, as described in the following table. Use the
new properties instead.
Recommended Property Obsolete Property
net.numOutputs net.numTargets
net.outputConnect net.targetConnect
net.outputs net.targets
Neural Network Toolbox™ Release Notes
Version 5.0.2 (R2007a) Neural Network Toolbox™ Software
This table summarizes what’s new in Version 5.0.2 (R2007a):
New Features
and Changes
No No Bug Reports None
Version
Compatibility
Considerations
There are no new features or changes in this version.
Fixed Bugs and
Known Problems
Related Documentation at
Web Site
17
Version 5.0.1 (R2006b) Neural Network Toolbox™ Software
Version 5.0.1 (R2006b) Neural Network Toolbox™
Software
This table summarizes what’s new in Version 5.0.1 (R2006b):
New Features
and Changes
No No Bug Reports None
Version
Compatibility
Considerations
There are no new features or changes in this version.
Fixed Bugs and
Known Problems
Related Documentation at
Web Site
18
Neural Network Toolbox™ Release Notes
Version 5.0 (R2006a) Neural Network Toolbox™ Software
This table summarizes what’s new in Version 5.0 (R2006a):
New Features
and Changes
Yes
Details below
Version
Compatibility
Considerations
Yes—Details
Fixed Bugs and
Known Problems
Related Documentation at
Web Site
Bug Reports None
labeled as
Compatibility
Considerations
,
below. See also
Summary.
New features and changes introduced in this version are
• “Dynamic Neural Networks” on page 19
• “Wizard for Fitting Data” on page 20
• “Data Preprocessing and Postprocessing” on page 20
• “Derivative Functions Are Obsolete” on page 21
Dynamic Neural Networks
Version 5.0 now supports these types of dynamic neural networks:
Time-Delay Neural Network
Both focused and distributed time-delay neural networks are now supported.
Continue to use the
networks. To create distributed time-delay neural networks, use the
function.
newfftd function to create focused time-delay neural
newdtdnn
Nonlinear Autoregressive Network (NARX)
To create parallel NARX configurations, use the newnarx function. To create
series-parallel NARX networks, use the
f
unction lets you convert NARX networks from series-parallel to parallel
configuration, which is useful for training.
newnarxsp function. The sp2narx
19
Version 5.0 (R2006a) Neural Network Toolbox™ Software
Layer Recurrent Network (LRN)
Use the newlrn function to create LRN networks. LRN networks are useful for
solving some of the more difficult problems in filtering and modeling
applications.
Custom Networks
The training functions in Neural Network Toolbox are enhanced to let you
train arbitrary custom dynamic networks that model complex dynamic
systems. For more information about working with these networks, see the
Neural Network Toolbox™ documentation.
Wizard for Fitting Data
The new Neural Network Fitting Tool is now available to fit your data using a
neural network. The Neural Network Fitting Tool is designed as a wizard and
walks you through the data-fitting process step by step.
To open the Neural Network Fitting Tool, type the following at the MATLAB
prompt:
®
20
nftool
Data Preprocessing and Postprocessing
Version 5.0 provides the following new data preprocessing and postprocessing
functionality:
dividevec Automatically Splits Data
The dividevec function facilitates dividing your data into three distinct sets to
be used for training, cross validation, and testing, respectively. Previously, you
had to split the data manually.
fixunknowns Encodes Missing Data
The fixunknowns f unction e n c o des missi n g values in y o ur data so t h a t 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.
Neural Network Toolbox™ 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. Use the new functions instead.
New Function Obsolete Functions
mapminmax premnmx
postmnmx
tramnmx
mapstd prestd
poststd
trastd
processpca prepca
trapca
Each new function is more efficient than its obsolete predecessors because it
accomplishes both preprocessing and postprocessing of the data. For example,
previously you used
premnmx to process a matrix, and then postmnmx to return
the data to its original state. In this release, you accomplish both operations
using
mapminmax; to return the data to its original state, you call mapminmax
again with
mapminmax('reverse',Y,PS)
'reverse' as the first argument:
Derivative Functions Are Obsolete
The following derivative functions are now obsolete:
ddotprod
dhardlim
dhardlms
dlogsig
21
Version 5.0 (R2006a) Neural Network Toolbox™ Software
dmae
dmse
dmsereg
dnetprod
dnetsum
dposlin
dpurelin
dradbas
dsatlin
dsatlins
dsse
dtansig
dtribas
Each derivative function is named by prefixing a d to the corresponding
function name. For example,
and
dsse calculated the derivative of the network performance function.
Compatibility Considerations
To calculate a derivative in this version, you must pass a derivative argument
to the function. For example, to calculate the derivative of a hyperbolic tangent
sigmoid transfer function
sse calculates the network performance function
A with respect to N, use this syntax:
22
A = tansig(N,FP)
dA_dN = tansig('dn',N,A,FP)
Here, the argument 'dn' requests the derivative to be calculated.
Neural Network Toolbox™ Release Notes
Version 4.0.6 (R14SP3) Neural Network Toolbox™ Software
This table summarizes what’s new in Version 4.0.6 (R14SP3):
New Features
and Changes
No No Bug Reports None
Version
Compatibility
Considerations
There are no new features or changes in this version.
Fixed Bugs and
Known Problems
Related Documentation at
Web Site
23
Compatibility Summary for Neural Network Toolbox™ Software
Compatibility Summary for Neural Network Toolbox™
Software
This table summarizes new features and changes that might cause
incompatibilities when you upgrade from an earlier version, or when you use
files on multiple versions. Details are provided with the description of the new
feature or change.
Version
(Release)
Latest Version
V6.0.4 (R2010a)
V6.0.3 (R2009b)
V6.0.2 (R2009a)
V6.0.1 (R2008b)
V6.0 (R2008a)
V5.1 (R2007b) See the
New Features and Changes with Version
Compatibility Impact
None
None
None
None
See the Compatibility Considerations subheading
for this new feature or change:
• “New Training GUI with Animated Plotting
Functions” on page 7
• “New Clustering Training, Initialization, and
Plotting GUI” on page 8
• “New Fitting Network, Plots and Updated Fitting
GUI” on page 9
for this new feature or change:
• “Simplified Syntax for Network-Creation Functions”
on page 10
Compatibility Considerations subheading
24
• “Properties for Targets Now Defined by Properties
for Outputs” on page 16
V5.0.2 (R2007a) None
V5.0.1 (R2006b) None
Neural Network Toolbox™ Release Notes
V5.0 (R2006a) See the
for this new feature or change:
• “mapminmax, mapstd, and processpca Are New” on
page 21
• “Derivative Functions Are Obsolete” on page 21
V4.0.6 (R14SP3) None
Compatibility Considerations subheading
25
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