Different results between y=net(test) and matrix multiplication of NN with same structures

Hi,

I have a neural netwok with following strctures 5 inputs 1 hidden layer with 17 neurons and 1 output.

I use sigmoid function from input to hidden layer and purelin function from hidden layer to output. After training the nerual network is done.

I apply yy= net(test) and i got the result. However when i extract the weight and baise and i do the matrix multipicaton for NN structure I got different result compared with yy=net(test). i did not know what are the reasons for differnt results.

This is my code

hidden_layer_output = logsig( net.IW{1}* test + net.b{1});

output = net.LW{2,1} * hidden_layer_output + net.b{2};

This is my the full code for training the NN

x = input1;

t = target1;

% Choose a Training Function

% For a list of all training functions type: help nntrain

% 'trainlm' is usually fastest.

% 'trainbr' takes longer but may be better for challenging problems.

% 'trainscg' uses less memory. Suitable in low memory situations.

trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation.

% Create a Fitting Network

hiddenLayerSize = 17;

net = fitnet(hiddenLayerSize,trainFcn);

% Setup Division of Data for Training, Validation, Testing

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 15/100;

net.divideParam.testRatio = 15/100;

net.layers{1}.transferFcn = 'logsig';

%net.layers{2}.transferFcn = 'purline';

% Train the Network

[net,tr] = train(net,x,t);

% Test the Network

y = net(x);

e=y-t;

mse1=immse(t,y);

rmse=sqrt(mse1);

weights = getwb(net)

Iw = net.IW

b1 = net.b(1)

Lw = net.Lw

b2 = net.b(2)

[HASAN AL-KAF]

i got the answer on way is to turn off the normaization using

net.inputs{1}.processFcns = {};

net.outputs{2}.processFcns = {};

thank you