The toolbox contains:

• Fast, robust, and easy-to-use training algorithms. 
• A number of different model structures for modelling of dynamic systems. 
• Validation of trained network models. 
• Estimation of the models's generalization ability. 
• Demonstration programs.

Neural Networks for Modelling and Control of Dynamic Systems
by Magnus Nørgaard, O. Ravn, N. K. Poulsen, and L. K. Hansen

is available on Springer-Verlag, London, in the series Advanced Textbooks in Control and Signal Processing.

Visit our homepage for the book

 
 

LET ME HAVE IT...

Version 2

Version 1.1

It appears that problems occur when trying to print the manuals on certain printers. I have therefore used the unix-command 'ps2pdf' to convert the manuals to pdf-format. View tutorial section or reference section. The manuals are included in postscript format in the zip-files above.

MEX files for version 1.1

• The toolbox is no longer dependent on the Signal Processing Toolbox.
• The training is more automatic (better stopping criteria have been introduced).
• Easier call of training algorithms.
• Options to training algorithms changed to an object oriented like fashion.
• Bug fixes and fine-tuning.

We encourage all users of the NNSYSID toolbox to write us about their successes (and failures?). We are very interested in hearing where the toolbox is used and for what type of applications. Since your comments very well may influence future releases of the toolbox this is also in your own interest! You can e-mail your experiences to the address listed at the bottom of this page.

 

AN ADD-ON FOR CONTROL DESIGN

If you are interested in neural networks for control we recommend that you download our NNCTRL toolkit. See our NNCTRL toolkit page for supplementary information.

 

FUNCTION OVERVIEW

The toolbox functions grouped by subject

FUNCTIONS FOR TRAINING
batbp	Batch version of the back-propagation algorithm
incbp	Recursive (/incremental) version of back-propagation
igls	Iterated Generalized Least Squares training of multi-output nets
marq	Levenberg-Marquardt method
marqlm	Memory-saving implementation of the Levenberg-Marquardt method
rpe	Recursive prediction error method

 

FUNCTIONS FOR PREPARATION OF DATA
dscale	Scale data to zero mean and variance one

 

FUNCTIONS FOR TRAINING MODELS OF DYNAMIC SYSTEMS
lipschit	Determine the lag space
nnarmax1	Identify a Neural Network ARMAX (or ARMA) model (Linear MA filter)
nnarmax2	Identify a Neural Network ARMAX (or ARMA) model
nnarx	Identify a Neural Network ARX (or AR) model
nnarxm	Identify a multi output Neural Network ARX (or AR) model.
nnigls	Iterated Generalized LS training of multi-output NNARX models.
nniol	Identify a Neural Network model suited for I-O linearization control
nnoe	Identify a Neural Network Output Error model
nnrarmx1	Recursive counterpart to NNARMAX1
nnrarmx2	Recursive counterpart to NNARMAX2
nnrarx	Recursive counterpart to NNARX
nnssif	Identify a NN State Space Innovations form model

 

FUNCTIONS FOR PRUNING NETWORKS
netstruc	Extract weight matrices from matrix of parameter vectors
nnprune	Prune models of dynamic systems with Optimal Brain Surgeon (OBS)
obdprune	Prune feed-forward networks with Optimal Brain Damage (OBD)
obsprune	Prune feed-forward networks with Optimal Brain Surgeon (OBS)

 

FUNCTIONS FOR EVALUATING TRAINED NETWORKS
fpe	FPE estimate of the generalization error for feed-forward nets
ifvalid	Validation of models generated by NNSSIF
ioleval	Validation of models generated by NNIOL
kpredict	k-step ahead prediction of dynamic systems.
loo	Leave-One-Out estimate of generalization error for feed-forward nets
nneval	Validation of feed-forward networks (trained by marq,rpe,bp)
nnfpe	FPE for I/O models of dynamic systems
nnloo	Leave-One-Out estimate for NNARX models.
nnsimul	Simulate model of dynamic system from sequence of inputs
nnvalid	Validation of I/O models of dynamic systems
wrescale	Rescale weights of trained network
xcorrel	Calculates high-order cross-correlation functions

 

MISCELLANOUS FUNCTIONS
crossco	Calculate correlation coefficients.
drawnet	Draws a two layer neural network
getgrad	Derivative of network outputs w.r.t. the weights
pmntanh	Fast tanh function
settrain	Set parameters for training algorithms.

 

DEMOS
test1	Demonstrates different training methods on a curve fitting example
test2	Demonstrates the NNARX function
test3	Demonstrates the NNARMAX2 function
test4	Demonstrates the NNSSIF function
test5	Demonstrates the NNOE function
test6	Demonstrates the effect of regularization by weight decay
test7	Demonstrates pruning by OBS on the sunspot benchmark problem

 

MORE INFORMATION

For more information, please contact

LINKS

• Me
• Homepage for the book "Neural Networks for Modelling and Control of Dynamic Systems.
• The MathWorks, Inc. The creator of MATLAB.
• MATLAB Central, file exchange. A lot of free MATLAB functions available for downloaded
• NNCTRL toolkit. An add-on to NNSYSID for control system design.
• ENTOOL. is a software package for ensemble regression modelling. The toolbox uses a subset of the NNSYSID toolbox.
• Some demo-files for "Predictive Control with Neural Networks" provided by Alexander Bollig.
• NNCTRL toolkit. An add-on to NNSYSID for control system design.
• The Automation Section at Ørsted-DTU.
• Section for Digital Signal Processing at the Department of Informatics and Mathematical Modelling (IMM), DTU. Main neural network group at DTU. Download publications etc.