Data set attached.

I have a trained neural network:

`% Fit a feedforward neural network to a set of FDM processing data.`

% Analysis of errors, computationally and visually.

%

% Input: fdm_trainingdata.m file

% Table with columns for:

% layer thickness [mm], deposition speed [mm/s], elastic modulus [MPa],

% tensile strength [MPa]

% Separate arrays that define the 5 layer thicknesses and 5 deposition

% speeds

%% Output:

% surface plots of neural network fit of elastic modulus and strength

% errors between predictions of modulus and measured values

% quadratic polynomial regression fits of modulus and strength

clear% Read file; variables are 'trainingdata,' 'missing,' 'layerthick,'

% 'speed' and 'inputmat'

run('fdm_trainingdata.m'); inputs = trainingdata(:,1:2)'; targets = trainingdata(:,3:4)';%nvar = length(layerthick);

% Create a Fitting Network

hiddenLayerSize = [15 15]; net = fitnet(hiddenLayerSize);% Set up Division of Data for Training, Validation, Testing

net.divideParam.trainRatio = 1; net.divideParam.valRatio = 0; net.divideParam.testRatio = 0; % Train the Network

[net,tr] = train(net,inputs,targets); % Test the Network

outputs = net(inputs); errors = gsubtract(outputs,targets); performance = perform(net,targets,outputs) % View the Network

view(net)

I need generate the modulus and strength prediction plots using matrices that are at least 25×26 in size.

How do I generate predictions using the trained network? I need to generate input vectors of (25×25 =) 625 elements so that I can plot the results using surf command.

## Best Answer