I see that you are getting different results when you try to compute the ANN layer manually. I looked at the data and the model you provided. You have calculated the main step correctly but you have missed the input preprocessing and output post processing steps. That is why you are getting different results. You can see the preprocessing functions applied in the model by using the following command:
net.inputs{1}.processFcns
Similarly post-processing functions can be seen using the below command:
net.outputs{1}.processFcns
You will see that in both the cases the "mapminmax" function is used. So to get accurate results you need to use the function in your calculations as well.
Before calculating the output you can process the input using the below code:
inputSettings = net.inputs{1}.processSettings{1};
ymin = inputSettings.ymin; % Minimum of inputs before normalization
ymax = inputSettings.ymax; % Maximum of inputs before normalization
xmin = inputSettings.xmin;
xmax = inputSettings.xmax;
originalInputs = (x - xmin) .* ((ymax-ymin)./(xmax-xmin)) + ymin
After this execute your earlier code as follows:
output = net.IW{1}*(x) + net.b{1}
After this to perform the post processing, you can refer to the below code:
outputSettings = net.outputs{1}.processSettings{1};
ymin = outputSettings.ymin; % Minimum of outputs before normalization
ymax = outputSettings.ymax; % Maximum of outputs before normalization
xmin = outputSettings.xmin; % Typically -1
xmax = outputSettings.xmax; % Typically 1
originalOutputs = (output - ymin) .* ((xmax-xmin)./(ymax-ymin)) + xmin
Now you can compare the manually calculated output to the "y" matrix and find that the values are almost equal.
You can check the construction of the ANN in the Simulink model I have attached. I have constructed it using your ANN.
For more information on "mapminmax" function refer the below documentation:
I hope this helps.
