LSTM Hysteresis curve modeling

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behrad rashedi
behrad rashedi on 24 Feb 2022
Answered: Milan Bansal on 3 Jan 2024
Hello,
I need to model a hysteresis curve by LSTM neural network can anyone help me with that?
I've read matlab documentations but unf I could'nt find my case. In face I don't know how to define properly network architucture (layer's).
Thanks for your help
  2 Comments
yanqi liu
yanqi liu on 25 Feb 2022
yes,sir,may be upload your data mat file to analysis
behrad rashedi
behrad rashedi on 25 Feb 2022
it is two columns of simple numbers as input and output.

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Answers (1)

Milan Bansal
Milan Bansal on 3 Jan 2024
Hi behrad,
It is my understanding that you want to build a Long Short-Term Memory (LSTM) Network to model a hysteresis curve using your data.
Below are high level steps to build an LSTM Model to model the hysteresis curve:
  1. Preprocess Data: Normalize the data if necessary and structure it into sequences that the LSTM can process.
  2. Create a sequence-to-sequence LSTM network architecture using Deep Learning Toolbox. The design includes choosing the number of LSTM layers, the number of hidden units in each layer, and other hyperparameters.
  3. Train the LSTM network using the training data.
Please refer to the pseudo code in the code snippet below: -
% Normalize data (if necessary)
inputDataNorm = normalize(inputData);
outputDataNorm = normalize(outputData);
% Prepare sequences - t
XTrain = ... % Your input sequences
YTrain = ... % Your output sequences
% Define LSTM network architecture
layers = [
sequenceInputLayer(size(XTrain,1))
lstmLayer(50,'OutputMode','sequence')
fullyConnectedLayer(1)
regressionLayer
];
% Specify training options
options = trainingOptions('adam', ...
'MaxEpochs',100, ...
'GradientThreshold',1, ...
'InitialLearnRate',0.005, ...
'LearnRateDropPeriod',125, ...
);
% Train LSTM network
net = trainNetwork(XTrain, YTrain, layers, options);
Please refer to the documentation below to learn more about LSTM Network.
Please refer to the documentation below to learn more about "lstmLayer".
Hope it helps!

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