sensitivity analysis, multilayer, feed-forward, back-propagation neural network using MATLAB.

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Majda Eljaat
Majda Eljaat el 6 de Abr. de 2014
Editada: Andualem alemu el 7 de Abr. de 2015
How can I carry out a sensitivity analysis, that is, the effect of input parameters on the output of a multilayer, feed-forward, back-propagation neural network using MATLAB. What is the code for this, or is there any inbuilt function to carry out ?

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Greg Heath
Greg Heath el 11 de Abr. de 2014
For a simple MIMO MLP, the I/O relationship is
[ I N ] = size(x);
[ O N ] = size(y);
y = b2 + LW*tanh(b1+IW*x);
Just take gradients with respect to whatever parameter component you are interested in.
Hope this helps.
Thank you for formally accepting my answer
Greg
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Greg Heath
Greg Heath el 7 de Abr. de 2015
I am confused. Yesterday you wrote
" this makes it clear. thank you"
but today you wrote
"...are not clear."
What has changed? More to the point: Did you learn anything at all from the formula? If so, what?
The following should help:
h = tansig( b1 + IW*x ); % HIDDEN LAYER
y = b2 + LW*h; % OUTPUT LAYER
Andualem alemu
Andualem alemu el 7 de Abr. de 2015
Editada: Andualem alemu el 7 de Abr. de 2015
I am sorry that I am bothering you too much. But it is because I am absolute beginner and so curious to know, work on and understand MLP and ensemble MLP for time series forecasting!!
Dear sir my question is in the formula:
y = b2 + LW*tanh(b1+IW*x);
What is Y, b2, LW, b1, IW and x ?
what are they representing and how can I find their value?

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