Multi variable prediction LSTM

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Riccardo
Riccardo 2025-3-9
回答： praguna manvi 2025-3-12
Hi,
I am trying to train a network on a dataset. I would like to predict the coefficients of a spline that represent a contact force between to impacting objects and the total contact time. The spline coefficient vector has variable length depending on the case but as of now I get predictions of the same length ( set to the maximum lenght of the coefficients vector). This is the code I've written
inputTable = table;
predictorNames = {'v_i', 'E_tip', 'rho_tip', 'v_tip', 'Y_tip', ...
'Radius', 'E_plate', 'rho_plate', 'v_plate', 'Y_plate', 'Insulator'};
predictors = inputTable{:, predictorNames}';
maxLen = max(cellfun(@numel, inputTable.Sp));
responseCoeffs = zeros(maxLen, length(inputTable.Sp));
scalarResponse = inputTable.ContactTime;
for i = 1:length(inputTable.Sp)
coeffs = inputTable.Sp{i};
responseCoeffs(1:numel(coeffs), i) = coeffs;
end
predictors = normalize(predictors, 2);
responseCoeffs = normalize(responseCoeffs, 2);
scalarResponse = normalize(scalarResponse);
numFeatures = size(predictors, 1);
numSplineCoeffs = maxLen;
% LSTM
layers = [
sequenceInputLayer(numFeatures)
lstmLayer(50, 'OutputMode', 'sequence')
fullyConnectedLayer(100)
reluLayer
fullyConnectedLayer(numSplineCoeffs + 1)
regressionLayer
];
options = trainingOptions('adam', ...
'MaxEpochs', 1000, ...
'MiniBatchSize', 32, ...
'Shuffle', 'every-epoch', ...
'Plots', 'training-progress', ...
'Verbose', true);
fullResponse = [responseCoeffs; scalarResponse'];
net = trainNetwork(predictors, fullResponse, layers, options);
This is my first time trying to train a network so any advice even on where to find more information on how to select the corret type of network would be much appreciated.
Thanks in advance!

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praguna manvi
praguna manvi 2025-3-12
Hi @Riccardo,
Choosing the right architecture depends on the complexity of the problem. Here are some resources that discuss which approach to take based on the type of problem:
Since you have only 1800 samples, it would be easier to fit them with a lighter network, as more parameters require more data. Also, since R2023b, it is recommended to use "trainnet" instead. Refer to the following link for more examples:

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