Version 5.0 (R2006a) Neural Network Toolbox™ Software

This table summarizes what's new in Version 5.0 (R2006a):

New Features and Changes	Version Compatibility Considerations	Fixed Bugs and Known Problems	Related Documentation at Web Site
Yes Details below	Yes--Details labeled as Compatibility Considerations, below. See also Summary.	Bug Reports	None

New features and changes introduced in this version are

• Dynamic Neural Networks
• Wizard for Fitting Data
• Data Preprocessing and Postprocessing
• Derivative Functions Are Obsolete

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 newfftd function to create focused time-delay neural networks. To create distributed time-delay neural networks, use the newdtdnn function.

Nonlinear Autoregressive Network (NARX)

To create parallel NARX configurations, use the newnarx function. To create series-parallel NARX networks, use the newnarxsp function. The sp2narx function lets you convert NARX networks from series-parallel to parallel configuration, which is useful for training.

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:

• 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 function encodes missing values in your data so that 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.

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 'reverse' as the first argument:

• mapminmax('reverse',Y,PS) 
  

Derivative Functions Are Obsolete

The following derivative functions are now obsolete:

• ddotprod
  dhardlim
  dhardlms
  dlogsig
  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, sse calculates the network performance function 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 A with respect to N, use this syntax:

• A = tansig(N,FP)
  dA_dN = tansig('dn',N,A,FP)
  

Here, the argument 'dn' requests the derivative to be calculated.

Provide feedback about this page

Version 5.0.1 (R2006b) Neural Network Toolbox™ Software

Version 4.0.6 (R14SP3) Neural Network Toolbox™ Software

© 1984-2009- The MathWorks, Inc.    -   Site Help   -   Patents   -   Trademarks   -   Privacy Policy   -   Preventing Piracy   -   RSS