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Rprop

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Rprop

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06 Aug 2011 (Updated )

Rprop training for Artificial Neural Networks

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Description

One of the training methods for Artificial Neural Networks is the Resilient Propagation (Rprop). Rprop is usually faster compared to the classical Backpropagation. In this package 4 different Rprop algorithms present in the literature are specifically implemented to train an ANN: Rprop+, Rprop-, IRprop+, IRprop-.

MATLAB release MATLAB 7.12 (R2011a)
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Comments and Ratings (5)
27 Mar 2015 Idil Selin

Yes, it works. Thank you very much:)
Idil

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27 Mar 2015 Roberto Calandra

Hi Idil,
when you call RNN.lab2class(train_out); RNN is not supposed to be the NN, but just the function lab2class inside the folder +RNN. Did you added the folder where the package is to your path? remember that you cannot add just the package folder.

For more info: http://se.mathworks.com/help/matlab/matlab_oop/scoping-classes-with-packages.html

Let me know if it is working :)

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22 Mar 2015 Idil Selin

Dear Roberto,
thank you very much for your code. Unfortunately, I do NOT manage to run it. Can you direct me? Where shall I start from? I guess that I need to create a RNN first. Shall I call the init_nn() function from the command window? Which are the parameters?
Demo_RProp_1 fails at line 26, where it calls RNN.lab2class(train_out); ... obviously, since there is no NN.
Thank you in advance for your explanations.
Idil

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29 Aug 2013 Roberto Calandra

Hi Matthew,
Yes, the answer is that you can use the same data set for both training and testing. You can do that by setting the test set (test_in in the Demos) equal to the training set (test_in).

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26 Aug 2013 Matthew

Hi, was wondering if i have a set of data with 150 samples. Then can i use your network to perform training then after that perform testing to compare the output of the testing set matching the result.

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Updates
12 Aug 2011 1.2

Improved the documentation and added a second demo. only small changes in the code.

23 Aug 2011 1.3

Another bit of documentation added

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