Integrating a LSTM layer into a NARX network

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GIROLAMO ODDO
GIROLAMO ODDO 2022-8-8
回答： Conor Daly 2023-3-31
Hi, is it possible to integrate an LSTM layer into this type of network?
obtaining a network like:
Input Layer - NARX - LSTM - Output Layer ?
thanks to anyone who can help me
I attach my current code where I would like to insert the LSTM layer:
_______________________________________________________________________________________
% Solve an Autoregression Problem with External Input with a NARX Neural Network
%
% This script assumes these variables are defined:
%
% NN-IN - input time series.
% NN-TARG - feedback time series.
clear; clc; format long;
IN = readmatrix('NN-IN.xlsx');
TARG = readmatrix('NN-TARG.csv');
X = tonndata(IN,false,false);
T = tonndata(TARG,false,false);
% Choose a Training Function
% 'trainscg' uses less memory. Suitable in low memory situations.
% 'traingdx' Gradient descent with momentum and adaptive learning rate backpropagation
trainFcn = 'trainscg'; % Scaled conjugate gradient backpropagation.
% Create a Nonlinear Autoregressive Network with External Input
inputDelays = 1:2;
feedbackDelays = 1:2;
hiddenLayerSize = [30,10];
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn);
% Prepare the Data for Training and Simulation
[x,xi,ai,t] = preparets(net,X,{},T);
% Setup Division of Data for Training, Validation, Testing
net.divideFcn = 'divideblock';
net.divideMode = 'time';
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
%Tolerance
net.trainParam.max_fail=3;
tic
% Train the Network
[net,tr] = train(net,x,t,xi,ai);
toc
% Test the Network
y = net(x,xi,ai);
e = gsubtract(t,y);
% net.performParam = 'normalized';
% net.performFcn = 'mse';
performance = perform(net,t,y);
% View the Network
view(net);
  4 个评论
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sam l
sam l 2022-9-28
I am looking to use NARX and LSTM , but yet to figure out it .
I was looking at CNN+RNN and thought if i can be done
https://www.mathworks.com/matlabcentral/fileexchange/91360-time-series-forecasting-using-hybrid-cnn-rnn

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回答（1 个）

Conor Daly
Conor Daly 2023-3-31
Hi Girolamo,
You can combine NARX and LSTM architectures within dlnetwork. Note that a NARX network is essentially a 1D convolution over a concatenation of the input sequence x and the time steps t. Here's an example which you can run using the L-BFGS optimizer which was released with R2023a:
%% Get MagLev data.
[x,t] = maglev_dataset;
x = [x{:}];
t = [t{:}];
X1 = dlarray(x(:, 1:end-1), 'CTB');
X2 = dlarray(t(:, 1:end-1), 'CTB');
T = dlarray(t(:, 3:end), 'CTB');
%% Construct NARX-LSTM dlnetwork.
layers = [ sequenceInputLayer(1, Name="xin", MinLength=2)
concatenationLayer(1, 2, Name="cat")
convolution1dLayer(2, 10)
tanhLayer()
lstmLayer(16)
fullyConnectedLayer(1) ];
lg = layerGraph(layers);
lg = addLayers(lg, sequenceInputLayer(1, Name="tin", MinLength=2));
lg = connectLayers(lg, "tin", "cat/in2");
net = dlnetwork(lg);
analyzeNetwork(net, X1, X2)
%% Train using L-BFGS.
maxEpochs = 150;
solverState = [];
lossFcn = @(net)dlfeval(@modelLoss, net, X1, X2, T);
monitor = trainingProgressMonitor;
monitor.Metrics = "TrainingLoss";
monitor.XLabel = "Epoch";
for epoch = 1:maxEpochs
[net, solverState] = lbfgsupdate(net, lossFcn, solverState);
recordMetrics(monitor, epoch, TrainingLoss=solverState.Loss);
end
%% Open-loop inference.
Y = predict(net, X1, X2);
yopen = extractdata(Y(:));
figure;
plot(yopen, '.'), hold on, plot(t(3:end))
%% Model loss function.
function [loss, grad] = modelLoss(net, X1, X2, T)
Y = predict(net, X1, X2);
loss = l2loss(Y, T);
grad = dlgradient(loss, net.Learnables);
end

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