Obtaining an analytical function of regression and Understanding the reversing concept of the normalisation (of input data) automatically done by Matlab Neural Network.

I have a problem in understanding the reversing of the normalisation (of input data) automatically done by Matlab. I have followed the questions/answers presented in matworks.com website (using repmap, mapping and reverse mapping functions) but they does not work. if you can help me, this is my code:

entree=xlsread('........\database.xlsx',1); % inputsize=[24960    8]
sortie=xlsread('........\database.xlsx',2); % outputsize=[24960    1]
 n=6 ;
 net=fitnet(n); 
 [net,TR]=train(net,entree',sortie');
 %evaluation of the analytical function
  x=[ 1100 , 1155 , 10 , 1 , 0 , 0.7 ,  343.7508 , 1.00017 ];
  b1 = net.b {1};   %size=[4  1]
  b2 = net.b {2};    %size=[1  1]
  IW = net.IW {1,1};  %size=[4  8]
  LW = net.LW {2,1};   %size=[1  4]
  a1=IW*x'+b1;
  y=purelin(LW*(tansig(a1))+b2); %  comparison with net(x')~ 10.37error=abs(net(x')-y);

[Mohamed El Ibrahimi]

I have found the response to my question, we can find the right solution by mapping and reverse mapping

entree=xlsread('O:\Stage\database.xlsx',1);
sortie=xlsread('O:\Stage\database.xlsx',2);
entree=entree';
sortie=sortie';
N=length(sortie);

net=fitnet(n);
net.numLayers;
[net,TR]=train(net,entree,sortie,'UseParallel','yes');

%%%%%%%%  do and extract Mapping settings %%%%
[entree_map,entree_setMap] = mapminmax (entree);
[sortie_map,sortie_setMap] = mapminmax (sortie);

%%%  apply mapping setting + regression Modele test + mapping reverse %%%%%%%%%%%%%%%%
e=[1155 10 0.7 1.00017];
emap= mapminmax ('apply',e',entree_setMap);

b1 = net.b {1};
b2 = net.b {2};
IW = net.IW {1,1};
LW = net.LW {2,1};

B1=repmat(b1,1,N);
B2=repmat(b2,1,N);

a1=IW*emap+b1
h1=tansig(a1);
a2=LW*h1+b2;
h3_map=purelin(a2);

h= mapminmax('reverse',h3_map,sortie_setMap)
y=net(e')

, but it is a nother way to do it, and I think is the best:

https://fr.mathworks.com/matlabcentral/answers/311783-neural-network-how-can-i-get-the-correct-output-answer-without-using-the-function-sim-neural-net#answer_242993