Магистерская диссертация тема работы Разработка цифрового двойника технологического процесса с использованием производственных данных

Окончание приложения В 
(справочное) 
Скрипт нейронной сети 
% Closed Loop Network
% Use this network to do multi-step prediction.
% The function CLOSELOOP replaces the feedback input with a direct
% connection from the output layer.
netc = closeloop(net); 
netc.name = [net.name 
' - Closed Loop'
]; 
view(netc) 
[xc,xic,aic,tc] = preparets(netc,{},{},T); 
yc = netc(xc,xic,aic); 
closedLoopPerformance = perform(net,tc,yc) 
[x1,xio,aio,t] = preparets(net,{},{},T); 
[y1,xfo,afo] = net(x1,xio,aio); 
[netc,xic,aic] = closeloop(net,xfo,afo); 
[y2,xfc,afc] = netc(cell(0,5),xic,aic); 
nets = removedelay(net); 
nets.name = [net.name 
' - Predict One Step Ahead'
]; 
view(nets) 
[xs,xis,ais,ts] = preparets(nets,{},{},T); 
ys = nets(xs,xis,ais); 
stepAheadPerformance = perform(nets,ts,ys) 
if
(false) 
genFunction(net,
'myNeuralNetworkFunction'
); 
y = myNeuralNetworkFunction(x,xi,ai); 
end
if
(false) 
genFunction(net,
'myNeuralNetworkFunction'
,
'MatrixOnly'
,
'yes'
); 
x1 = cell2mat(x(1,:)); 
xi1 = cell2mat(xi(1,:)); 
y = myNeuralNetworkFunction(x1,xi1); 
end
if
(false) 
gensim(net); 
end