Hi Tobias,
Instead of using a "sequenceInputLayer", you can use a standard "feedforwardnet" in MATLAB. You can follow the below steps to restructure your approach:
- Combine your input data ("rotational speed", torque, time) into a single matrix where each row represents a different operating point and ensure your target data (temperature) is in a corresponding vector.
- Use a standard feedforward neural network with input and output layers designed for regression and use the "train" function instead of "trainnet".
Here's is a sample example MATLAB code to demonstrate the same:
% Example Data
% Assuming you have 10 operating points
rotational_speed = [2000, 4000, 6000, 8000, 10000, 12000, 14000, 16000, 18000, 20000]; % Your actual data here
torque = repmat(50, 1, 10); % Example constant torque
time = repmat(10, 1, 10); % Example constant time
temperature = [70, 75, 80, 85, 90, 95, 100, 105, 110, 115]; % Your target temperature data
% Combine inputs into a matrix (each row is a feature, each column is a sample)
inputs = [rotational_speed; torque; time];
% Target data
targets = temperature;
% Create a Feed-Forward Neural Network
hiddenLayerSize = 10; % Adjust as needed
net = feedforwardnet(hiddenLayerSize);
% Train the Network
[net, tr] = train(net, inputs, targets);
% View the Network
view(net);
% Test the Network
outputs = net(inputs);
errors = gsubtract(targets, outputs);
performance = perform(net, targets, outputs);
% Display Results
disp('Performance:');
disp(performance);
You may refer to the attached output images and also the following MathWorks documentation links to have a better understanding on "feedforwardnet" and "train" functions:
