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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Greg Heath
2012 年 11 月 20 日
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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その他の回答 (4 件)
Greg Heath
2012 年 11 月 20 日
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
2012 年 11 月 20 日
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Greg Heath
2012 年 11 月 20 日
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
2013 年 5 月 16 日
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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