MATLAB: Neural Network: Closed Loop vs. Open Loop

closed loopneural networkopen loop

Hi! Just wanna ask a really simple question, as I am extremely new to Neural Networks so it would be appreciated if the answer can be worded in layman's terms. 1. What's the difference between closed and open loop feedback in Matlab's neural network?
2. If I'm using the NARX function in Matlab's GUI, the network layout on the welcome page shows a closed loop but why does the generated script in the end implies that it was trained under open loop conditions?(to predict multiple timesteps into the future would require closing the loop). Below is a section from the generated script of Matlab's GUI NARX function.
    % Closed Loop Network
  % Use this network to do multi-step prediction.
  % The function CLOSELOOP replaces the feedback input with a direct
  % connection from the outout layer.
  netc = closeloop(net);
  netc.name = [net.name ' - Closed Loop'];
  view(netc)
  [xc,xic,aic,tc] = preparets(netc,X,{},T);
  yc = netc(xc,xic,aic);
  closedLoopPerformance = perform(net,tc,yc)
% Multi-step Prediction
% Sometimes it is useful to simulate a network in open-loop form for as
% long as there is known output data, and then switch to closed-loop form
% to perform multistep prediction while providing only the external input.
% Here all but 5 timesteps of the input series and target series are used
% to simulate the network in open-loop form, taking advantage of the higher
% accuracy that providing the target series produces:
numTimesteps = size(x,2);
knownOutputTimesteps = 1:(numTimesteps-5);
predictOutputTimesteps = (numTimesteps-4):numTimesteps;
X1 = X(:,knownOutputTimesteps);
T1 = T(:,knownOutputTimesteps);
[x1,xio,aio] = preparets(net,X1,{},T1);
[y1,xfo,afo] = net(x1,xio,aio);
% Next the the network and its final states will be converted to
% closed-loop form to make five predictions with only the five inputs
% provided.
x2 = X(1,predictOutputTimesteps);
[netc,xic,aic] = closeloop(net,xfo,afo);
[y2,xfc,afc] = netc(x2,xic,aic);
multiStepPerformance = perform(net,T(1,predictOutputTimesteps),y2)
% Alternate predictions can be made for different values of x2, or further
% predictions can be made by continuing simulation with additional external
% inputs and the last closed-loop states xfc and afc.
% Step-Ahead Prediction Network
% For some applications it helps to get the prediction a timestep early.
% The original network returns predicted y(t+1) at the same time it is
% given y(t+1). For some applications such as decision making, it would
% help to have predicted y(t+1) once y(t) is available, but before the
% actual y(t+1) occurs. The network can be made to return its output a
% timestep early by removing one delay so that its minimal tap delay is now
% 0 instead of 1. The new network returns the same outputs as the original
% network, but outputs are shifted left one timestep.
nets = removedelay(net);
nets.name = [net.name ' - Predict One Step Ahead'];
view(nets)
[xs,xis,ais,ts] = preparets(nets,X,{},T);
ys = nets(xs,xis,ais);

Best Answer

Unfortunately, the documentation is not as clear as it could be.
OPENLOOP: The desired output, AKA the delayed target, is used as an additional input. The OL net will produce output for the common time extent of the input and target.
CLOSELOOP: The delayed target input is replaced by a direct delayed output connection. The CL net will produce output for the time extent of the input.
Hope this helps.
Thank you for formally accepting my answer
Greg
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