I understand that you are looking to train a model that takes an initial condition vector as input and outputs a corresponding sequence. This is a classic case of a "sequence generation" problem, which can effectively be modeled using "LSTM-based neural networks" provided by MATLAB’s Deep Learning Toolbox. Do note that there is no "one-line" or "prebuilt function" for "vector-to-sequence" generation.
Kindly refer to the following sample code to build such a model:
% Example input: Initial condition vectors
XTrain = {rand(10,1), rand(10,1), rand(10,1)}; % Cell array of 10x1 vectors
% Example output: Corresponding sequences
YTrain = {rand(1,20), rand(1,20), rand(1,20)}; % Cell array of 1x20 sequences
% Define network layers
layers = [
sequenceInputLayer(10) % Input size = 10
fullyConnectedLayer(100)
lstmLayer(100,'OutputMode','sequence')
fullyConnectedLayer(1)
regressionLayer];
% Specify training options
options = trainingOptions('adam', ...
'MaxEpochs', 100, ...
'MiniBatchSize', 16, ...
'SequenceLength', 'longest', ...
'Shuffle', 'every-epoch', ...
'Verbose', false, ...
'Plots', 'training-progress');
% Train the model
net = trainNetwork(XTrain, YTrain, layers, options);
% Predict output sequence for a new input vector
newInput = rand(10,1);
YPred = predict(net, newInput);
This setup allows the network to learn the mapping from static condition vectors to dynamic output sequences. The "sequenceInputLayer" and "lstmLayer" components are particularly well-suited for such temporal prediction tasks.
For further reference, please refer to the following official documentation:
- https://www.mathworks.com/help/deeplearning/ref/lstmlayer.html
- https://www.mathworks.com/help/deeplearning/examples/sequence-to-sequence-regression-using-deep-learning.html
Cheers & Happy Coding!
