I used Neural Network fitting tool for training my data and got outputs for each target that i supplied to the network. Those outputs are well within the error range and give a good fit for the network. But, now i want to predict output based on input samples not included within the data set that i previously provided to the nnftool for getting the outputs. Please tell me how i can do that? The input samples are withing the training set range.
ANSWER
Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.
Incorrect understanding: Generalization: Ability to perform well on nontraining data Overfitting: Number of training equations, Ntrneq, not being sufficiently larger than the number of unknown weights, Nw, can be a cause of DECREASED generalization. Mitigation: Either increase Ndof and/or use validation stopping(default) and/or use regularization (e.g., TRAINBR)Insufficient information: size(input) = [ I N ] = [ ? ? ]
size(target) = [ O N ] = [ ? ? ]
default number of training examples Ntrn = N-2*round(0.15*N) = ?
number of training equations Ntrneq = Ntrn*O
reference mean-square errors
MSEtrn00 = mean(var(trntarget',1)) % Biased
MSEtrn00a = mean(var(trntarget',0))% DOF adjusted
MSEval00 = mean(var(valtarget',1)) % Unbiased
MSEtst00 = mean(var(tsttarget',1)) % Unbiased
Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.
size(input) = [ I N ] = [ ? ? ] size(target) = [ O N ] = [ ? ? ] default number of training examples Ntrn = N-2*round(0.15*N) = ? number of training equations Ntrneq = Ntrn*O reference mean-square errors MSEtrn00 = mean(var(trntarget',1)) % Biased MSEtrn00a = mean(var(trntarget',0))% DOF adjusted MSEval00 = mean(var(valtarget',1)) % Unbiased MSEtst00 = mean(var(tsttarget',1)) % Unbiased
SEE COMPLETE ANSWER CLICK THE LINK
Comments
Post a Comment