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nnsysid

version 1.0.0.0 (715 KB) by Magnus Norgaard
The NNSYSID toolbox contains a number of tools for identification of nonlinear dynamic systems with

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Updated 14 Apr 2003

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Neural Network Based System Identification Toolbox Version 2

The NNSYSID toolbox contains a number of tools for identification of nonlinear dynamic systems with neural networks. Several nonlinear model structures based on multilayer perceptron networks are provided and there are also many functions for model validation and model structure selection. The toolbox requires MATLAB 5.3 or higher. A manual (~110 pages, pdf format) accompanies the toolbox. More information can be found on
www.iau.dtu.dk/research/control/nnsysid.html

Cite As

Magnus Norgaard (2019). nnsysid (https://www.mathworks.com/matlabcentral/fileexchange/87-nnsysid), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (25)

This toolbox has been very useful but i want to use the nets in simulink. It is posible ? Thanks.

fabian

I contact you to ask you to please help me with the tool, after having the trained network as the selected model structure or network is exported to Simulink. thanks

Mr Smart

Amir

I have a problem in running the toolbox nnsysid. Actually I got the following messeges,If anyone knows the answer, then I would appreciate.

[w1,w2] = wrescale('nnoe',W1,W2,uscales,yscales,NN);
>> [thd,trv,fpev,tev,deff,pv] = ...
nnprune('nnoe',NetDef,W1,W2,u1s,y1s,NN,trparms,prparms,u2s,y2s);
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.639375e-016.
> In nnprune at 372

Network training started.

iteration # 1 W = 5.571e-002
iteration # 2 W = 4.712e-002
iteration # 3 W = 4.314e-002
iteration # 4 W = 3.508e-002
iteration # 5 W = 3.186e-002
iteration # 6 W = 2.975e-002
iteration # 7 W = 2.947e-002
iteration # 8 W = 2.904e-002
iteration # 9 W = 2.889e-002
iteration # 10 W = 2.883e-002
iteration # 11 W = 2.880e-002
iteration # 12 W = 2.869e-002
iteration # 13 W = 2.864e-002
iteration # 14 W = 2.863e-002
iteration # 15 W = 2.862e-002
iteration # 16 W = 2.859e-002
iteration # 17 W = 2.858e-002
iteration # 18 W = 2.855e-002
iteration # 19 W = 2.855e-002
iteration # 20 W = 2.853e-002
iteration # 21 W = 2.853e-002
iteration # 22 W = 2.852e-002
iteration # 23 W = 2.851e-002
iteration # 24 W = 2.850e-002
iteration # 25 W = 2.850e-002
iteration # 26 W = 2.849e-002
iteration # 27 W = 2.849e-002
iteration # 28 W = 2.849e-002
iteration # 29 W = 2.848e-002
iteration # 30 W = 2.848e-002
iteration # 31 W = 2.848e-002
iteration # 32 W = 2.848e-002
iteration # 33 W = 2.848e-002
iteration # 34 W = 2.847e-002
iteration # 35 W = 2.847e-002
iteration # 36 W = 2.847e-002
iteration # 37 W = 2.847e-002
iteration # 38 W = 2.846e-002
iteration # 39 W = 2.846e-002
iteration # 40 W = 2.846e-002
iteration # 41 W = 2.845e-002
iteration # 42 W = 2.845e-002
iteration # 43 W = 2.845e-002
iteration # 44 W = 2.845e-002
iteration # 45 W = 2.844e-002
iteration # 46 W = 2.844e-002
iteration # 47 W = 2.844e-002
iteration # 48 W = 2.844e-002
iteration # 49 W = 2.844e-002
iteration # 50 W = 2.843e-002

Network training ended.

Network training started.

??? Error using ==> nnoe
AN ERROR OCCURED IN A CMEX-PROGRAM

Error in ==> nnprune at 257
[W1,W2,dummy1,dummy2,dummy3] = nnoe(NetDef,NN,W1,W2,trparmsp,Y,U);

glanny Mangindaan

This tool is Good.

Jim Wicket

Does anyone know how the meaning of doing forecasting with the function 'kpredict' ? It seems to me that this function only predicts the observed data k-th step ahead. It means the prediction does not do anything beyond the last data point of the dataset "y" that was used to train this network. For example, I want to predict the next 20 step ahead, then prediction starts on the point y(21), y(22), y(23), ..., y(end). These values are more like overlaying the original data with a lag of the first 20 points, with the new set of predictions. It never does prediction for future time step, such as y(end + 1) , y(end + 2), y(end + 3), ..., y(end + k).

If anyone knows the answer, then I would appreciate if you can share your understanding with me and anyone else's who is trying to use the toolbox.

s p

Excellent tool.

Sergio Velásquez

Satja Lumbar

I find that validation needs an update to work properly on MATLAB 7 (I use 7.01). In maxy functions (such as nnvalid.m) "break;" must be raplaced with "return;" for them to work properly. Otherwise a very useful tool.

San dar

hi The Moon

mostafa mahi

mostafa mahi

Boo Chin Eng

Sorry, anybody knows how to do the k--step prediction for (NNARXM)....except dealing it with seperate MISO systems

Hami Golbayani

Boo Chin Eng

Very good! I am now doing some research in NMPC(Nonlinear model predictive control), it help me a lot. Thanks, Mr. Norgaard.

richard evans

good stuff!

Christian Kotz

linda Wang

very good!

shashi londe

BEST

bhanu BHANU

very usefull &excellent

YONG LI

good, Thank you!

Shi Zhw

Excellent!

Rama Mohan Rao

Shine Lu

Good work, especially for the manul.

Olivier Salvado

good job.

Updates

1.0.0.0

Minor fixes

mod desc

MATLAB Release Compatibility
Created with R13
Compatible with any release
Platform Compatibility
Windows macOS Linux
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