As predicted delayed outputs settle in NarX
Master NARX networks for time-series prediction Learn how delayed outputs are modeled and settle. Get a step-by-step guide to dynamic state transitions. Sta...
Master NARX networks for time-series prediction Learn how delayed outputs are modeled and settle. Get a step-by-step guide to dynamic state transitions. Sta...
[P, T ] = simplenarx_dataset; whos p= cell2mat(P); t = cell2mat(T); ID = 1:1 FD = 1:1 H = 5 NID= length(ID) NFD=length(FD) Nw = (NID*I+NFD*O+1)*H+(H+1)*O
rng(0) net = narxnet(ID,FD,H); [inputs,inputStates,layerStates,targets] = preparets(net,P,{},T); whos P T inputs inputStates layerStates targets
[ I N ] = size(inputs) [ O N ] = size(targets)
net.divideFcn='divideblock'; [trainInd,valInd,testInd] = divideblock(N,0.7,0.15,0.15); ttrn = targets(trainInd); tval = targets(valInd); ttest = targets(testInd); Ntst = length(ttrn) Nval = length(valInd) Ntst = length(testInd) Ntrneq = prod(size(ttrn)) % Ntrn*O Ndof = Ntrneq-Nw
ytrn00= mean(ttrn,2); Nw00 = size(ytrn00,2) Ndof00 = Ntrneq-Nw00 MSEtrn00 = sse(ttrn-ytn000)/Ntrneq MSEtrn00=mean(var(ttrn,1)) MSEtrn00a = sse(ttrn-ytrn00)/Ndof00 MSEtrn00a=mean(var(ttrn,0))
MSEval00 = mse(tval-ytrn00) MSEtst00 = mse(tttst-ytrn00) net.trainParam.goal = 0.01*Ndof*MSEtrn00a/Ntrneq; % R2trna >= 0.99 rng(0) [net,tr,Ys,Es,Xf,Af] = train(net,inputs,targets,inputStates,layerStates); outputs = net(inputs,inputStates,layerStates); errors = gsubtract(targets,outputs); MSE = perform(net,targets,outputs); MSEa=Neq*MSE/(Neq-Nw) R2=1-MSE/MSE00 R2a=1-MSEa/MSE00a
% 10. The DOF "a"djustment is only applied to the training data
% 11. Can delete the last 6 equations that refer to all of the data instead of the trn/val/tst division.
MSEtrn=tr.perf(end) MSEtrna = Ntrneq*MSEtrn/Ndof MSEval=tr.vperf(end) MSEtst=tr.tperf(end)
R2trn=1-MSEtrn/MSEtrn00 R2trna=1-MSEtrna/MSEtrn00a
R2val=1-MSEval/MSEval00 R2tst=1-MSEtst/MSEtst00
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