%% [NN]: MATLAB Code for Neural Network
 
 
function rul=NN(TransFunc,nh,outliCrite)
clear global;
global DegraUnit TimeUnit time y thres signiLevel ns ny nt
%=== PROBLEM DEFINITION 1 (Required Variables) ==============
WorkName=' ';            % work results are saved by WorkName
DegraUnit=' ';                             % degradation unit
TimeUnit=' ';               % time unit (Cycles, Weeks, etc.)
time=[ ]';     %[cv]: time at both measurement and prediction
y=[ ]';                              %[ny x 1]: measured data
nT= ;        % num. of training set including the current one
nt=[ ]';%[nT x 1]: num. of training data in each training set
thres= ;                         % threshold (critical value)
degraTrue=[ ]';             %[cv]: true values of degradation
signiLevel= ;          % significance level for C.I. and P.I.
ns= ;                 %  number of repetition for NN process
%=== PROBLEM DEFINITION 2 (Training Matrix) =================
xTrain=[ ]; yTrain=[ ];
%============================================================
% % % PROGNOSIS using NN
ny=length(y);
for i=1:ns;                                     %% NN Process
 disp(['repetition: ' num2str(i) '/' num2str(ns)]);
 % create FFNN 
 net=feedforwardnet(nh);
 net.layers.transferfcn=TransFunc;
 net.divideParam.valRatio=0.3; net.divideParam.testRatio=0;
 % train FFNN
[netModel,trainRecod]=train(net,xTrain',yTrain');
%=== PROBLEM DEFINITION 2 (Input Matrix for Prediction) =====
 for k=1:length(time(ny:end));      %% Degradation Prediction
  xNew=[ ];
  zHat(k,i)=sim(netModel,xNew'); 
 end;
%============================================================
end
z1=zHat(end,:);              %% Prediction Results Regulation
loca1=find(abs(z1-mean(z1)) < outliCrite*std(z1));
i0=1;
for i=1:ns;
 if y(1)-y(nt(1))>0; z2=wrev(zHat(:,i));
 else z2=zHat(:,i);
 end;
 if issorted(z2)==1; loca2(i0)=i; i0=i0+1; end
end
loca=intersect(loca1,loca2);                 %% Final Results
degraPredi=zHat(:,loca);
ns=size(degraPredi,2);
disp(['Final num. of samples: ' num2str(ns)])
% % % POST-PROCESSING
rul=POST([],degraPredi,degraTrue);       %% RUL & Result Disp
Name=[WorkName ' at ' num2str(time(ny)) '.mat']; save(Name);
end