LSTM Hysteresis curve modeling

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behrad rashedi 2022-2-24

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回答： Milan Bansal 2024-1-3

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

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yanqi liu 2022-2-25

yes，sir，may be upload your data mat file to analysis

behrad rashedi 2022-2-25

it is two columns of simple numbers as input and output.

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Answer 1

Milan Bansal 2024-1-3

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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:

Preprocess Data: Normalize the data if necessary and structure it into sequences that the LSTM can process.
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.
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); 