Is it Possible to extract Regression equation of the Regression Plots in Neural Network Toolbar
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Hi, I am new in using Neural Network tool bar , I have a difficulty. Eg : I have a Input Data set of 30*52 and my target data set is 30*16 . I train it and i get Regression plots. Once i get the regression plots is it possible to extract the equation from the plots. As it is specific to use Neural Network and execute the work i am badly needing to extract the data out of the Figure to a excel sheet. Kindly help me with the above Problem.
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Respuesta aceptada
Greg Heath
el 20 de Nov. de 2012
help regression
doc regression
type regression
should suffice. However, can also look at
help plotregression
doc plotregression
type plotregression
Hope this helps.
Thank you for formally accepting my answer
Greg
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Más respuestas (4)
Greg Heath
el 20 de Nov. de 2012
i am surprised and confused by your last setence.
The regression equation associated with the training function is output vs target, and is only valid for 1-D targets and outputs. Inputs are not involved.
The nonlinear I/O multivariate regression equation for the default fitnet or feedforwardnet is
y = b1 + LW*tansig(b1 + IW*x)
which cannot be decomposed into a sum of fuctions of single input variables.
Greg
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Vijayaraghavan
el 20 de Nov. de 2012
1 comentario
Greg Heath
el 20 de Nov. de 2012
I do not understand exactly what you want. The I/O equation I wrote is the default. Please write exactly what you are looking for in a similar form.
The general form is
y = activation2( b2 + LW * activation1( b1 + IW * x ) )
where
size(x) = [I N ]
size(IW) = [ H I ]
size(b1) = [ H N ] %repmat([H 1],1,N)
size(LW) = [ O H ]
size(b2) = [ O N ] %repmat([O 1],1,N)
Imran Babar
el 16 de Mayo de 2013
Hi I have a data of 649 patterns and with 31 inputs in 1 pattern and with 1 output only how I will draw the regression plot. Though I am using the following syntax but the graph is not a straight line
plotregression(targetdata,predicted,'Regression')
and my target data is like the following one
0 1 2 3 4 5 6 7 2 3 4 5 6 7 8 9 4 5 6 7 8 9 7 8 9 10 11 12 13 14 and so on upto 649 values for each input pattern of 31 values
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