Hi Simone,
To adapt your current deep learning model to take into account the "memory" effect of your electrical circuit by using a sequence of inputs while still predicting a single output value, you can modify the network architecture and the way you prepare your input data. Since you're interested in using a DNN and have mentioned that your input data is a single column vector, you'll need to reshape your data to create sequences of inputs.
Here's a step-by-step approach to achieve this:
1. Reshape Your Input Data
Create sequences of 300 past samples as inputs for predicting the current output.
sequenceLength = 300;
numSamples = length(input_data) - sequenceLength;
X = zeros(sequenceLength, numSamples);
Y = zeros(1, numSamples);
for i = 1:numSamples
X(:, i) = input_data(i:i+sequenceLength-1);
Y(:, i) = output_data(i+sequenceLength);
end
2. Modify the Network Architecture
Change sequenceInputLayer to featureInputLayer with input size equal to your sequence length (300).
layers = [...
featureInputLayer(sequenceLength)
fullyConnectedLayer(50)
tanhLayer
fullyConnectedLayer(100)
tanhLayer
fullyConnectedLayer(1)
regressionLayer
];
3. Training the Network
Adjust your training process to handle the reshaped data. If using custom training loops, process the data as sequences of 300 samples.
4. Prediction
Ensure the input data for prediction is reshaped into sequences of 300 samples as well.
Find below the documentation of featureInputLayer
Hope this Helps!
