how can use static feedforward neural network to predict futre observation
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Greg Heath el 30 de Mayo de 2015
The generic NEWFF and special cases that call it (e.g., NEWFIT(regression/curvefitting) and NEWPR(Classification/pattern-recognition) ) are obsolete.
Use the current special cases FITNET(regression/curvefitting) and PATTERNNET(Classification/pattern-recognition,...) that call the generic FEEDFORWARDNET.
In your case it looks like FITNET.
However, why don't you want to use a dynamic net?
Hope this helps.
Thank you for formally accepting my answer
Más respuestas (2)
Greg Heath el 30 de Jun. de 2015
Use one of these
help nndatasets (Also see : doc nndatasets)
Single time-series prediction involves predicting the next value of a time-series given its past values.
simplenar_dataset - Simple single series prediction dataset.
chickenpox_dataset - Monthly chickenpox instances dataset.
ice_dataset - Gobal ice volume dataset.
laser_dataset - Chaotic far-infrared laser dataset.
oil_dataset - Monthly oil price dataset.
river_dataset - River flow dataset.
solar_dataset - Sunspot activity dataset
Unlike the datasets for FITNET, PATTERNNET and NARXNET, I have not posted these NARNET data set sizes in the NEWSGROUP.
Word to the wise:
ALWAYS begin using ALL of the defaults. This will simplify your code so much that you will think that you know what you are doing. To see what I mean see the documentation examples at
help fitnet % default H = 10
help narnet % defaults FD = 1:2, H = 10
TYPICALLY, the only default you may have to change is, H, the number of hidden nodes!
However, if that doesn't work you may have to
1. Design 10 or more nets to mitigate unfortunate choices of random initial weights and random data divisions.
2. Determine the statistically significant lags from the target autocorrelation function as I have posted in the NEWSGROUP.
Hope this helps
If you want to accept this answer instead of the previous one, I don't mind
Greg Heath el 2 de Jul. de 2015
If you want to use the static net FITNET to predict d timesteps ahead of a single N timestep timeseries, use defaults and double ( NOT CELL ) variables
input = data( 1:N-d); % No transpose;
target = data( 1+d : N );
MSE00 = var(target',1) % Reference MSE
net = fitnet; % default H = 10
net.divideParam.valRatio = 10/100;
net.divideParam.testRatio = 20/100;
[net tr output error ] = train(net, input, target);
%output = net(input); error = target - output;
NMSE = mse(error)/MSE00 % Range [ 0 1 ]
R2 = 1- NMSE
% Rsquared = fraction of target variance modeled by the net
Hope this helps.