I want to make a prediction from a trained neural network and have the prediction displayed on a display block

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Olalekan 2025-1-27

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评论： Olalekan 2025-1-30

I am doing a school project using Neural networks to predict faults, I have already trained the neural network, but I am finding it difficult to use the trained model directly on simulink, using the predict blocks. I was able to use script to extract the data and make prediction and display on the workspace, but I want everything to run directly from simulink

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Answer 1

Krishna 2025-1-28

在 MATLAB Online 中打开

Hi Olalekan,

I understand that you are facing problems in converting your neural network into a Simulink model.

I assume that you are using the latest dlnetwork object provided in MATLAB to train your Neural Network. To know more about how to use ‘dlnetwork’ please look at this documentation on how to train simple Neural Networks using 'dlnetwork.'

https://in.mathworks.com/help/deeplearning/ug/train-neural-network-with-tabular-data.html

If you want to convert your dlnetwork to a Simulink block you can use the function given below provided by the Layers library in Simulink,

exportNetworkToSimulink(dlnetwork)

This will generate a Simulink block which you can further use in your Simulink model. To display the output of the network you can use a scope or a display block connected as output to this model.

Please look at the below documentation to know more regarding this,

https://in.mathworks.com/help/deeplearning/deep-learning-with-simulink.html

Hope this helps.

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Olalekan 2025-1-29

在 MATLAB Online 中打开

The output i got from the model is a 12 by 1 array , which i beleive is equivalent to the 12 classes i have/

i will provide the screenshot for the uput on a display module.

while following the example provided in the link, when i converted the data to .csv, it was showing some form of unrecognised characters, i used the .mat file to train the model.

i will provide the code i used for training the mode, and the screenshot for better understanding

% data preparation
load('Fault_Model_Data.mat'); 
labels_categorical = categorical(labels); % Ensure labels are categorical
data_table = array2table(data, 'VariableNames', {'CoefA', 'CoefB', 'CoefC', 'CoefG'});
data_table.Labels = labels_categorical;
% Split data into training and validation sets
train_ratio = 0.8;
cv = cvpartition(height(data_table), 'HoldOut', 1 - train_ratio);
train_data = data_table(training(cv), :);
val_data = data_table(test(cv), :);
% Extract features and labels for training
XTrain = table2array(train_data(:, 1:4)); % Features
YTrain = train_data.Labels; % Labels
% Extract features and labels for validation
XValidation = table2array(val_data(:, 1:4));
YValidation = val_data.Labels;
% Normalize features 
mu = mean(XTrain);
sigma = std(XTrain);
%XTrain = (XTrain - mu) ./ sigma;
%XValidation = (XValidation - mu) ./ sigma;
% Define the deep learning network architecture
layers = [...
    featureInputLayer(size(XTrain, 2), 'Normalization', 'none', 'Name', 'Input')
    fullyConnectedLayer(128, 'Name', 'FC1')
    reluLayer('Name', 'ReLU1')
    dropoutLayer(0.5, 'Name', 'Dropout1')
    fullyConnectedLayer(64, 'Name', 'FC2')
    reluLayer('Name', 'ReLU2')
    fullyConnectedLayer(numel(categories(YTrain)), 'Name', 'Output')
    softmaxLayer('Name', 'Softmax')
    classificationLayer('Name', 'Classification')];
% Define training options
options = trainingOptions('adam', ...
    'MaxEpochs', 50, ...
    'MiniBatchSize', 32, ...
    'InitialLearnRate', 0.001, ...
    'ValidationData', {XValidation, YValidation}, ...
    'ValidationFrequency', 10, ...
    'Plots', 'training-progress', ...
    'Verbose', true);
% Train the neural network
trainedNet = trainNetwork(XTrain, YTrain, layers, options);
% Save the trained network
save('TrainedFaultClassifier.mat', 'trainedNet');

I will provide few screenshot i took for some fault class.

C-G fault class

this is foe NORMAL class

Walter Roberson 2025-1-29

In that case, exportNetworkToSimulink did not exist yet.

As far as I know your only option is to upgrade MATLAB versions.

Olalekan 2025-1-30

Thank you @Walter Roberson, I will upgrade to the recent version and try it out.

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