Using LSTM for non-linear system identification

Hello, i need some steps and information to build a neural network lstm for nonlinear system identification. I have already the data set, training set and test set. This is my code, what i need to do more?Please tell me

clear all
clc
load('twotankdata.mat');
output=y;
input=u;
%normalizarea datelor de intrare
min_input = min(input);
max_input = max(input);
x_normalized=(input-min_input)/(max_input-min_input);
min_output=min(output);
max_output=max(output);
y_normalized=(output-min_output)/(max_output-min_output);
x_train=x_normalized(1:2250,:);
x_test=x_normalized(2251:3000,:);
y_train=y_normalized(1:2250,:);
y_test=y_normalized(2251:3000,:);
%Define LSTM Network Arhitecture
model=lstmLayer(100,'OutputMode','sequence','StateActivationFunction','tanh','GateActivationFunction','sigmoid');
numFeatures=size(x_train',2);
inputSize=1;
numHiddenUnits=70;
numClasses=1;
layer=[...
    sequenceInputLayer(inputSize)
    lstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    %softmaxLayer
    regressionLayer
    ];
options=trainingOptions('adam',...
    'ExecutionEnvironment','cpu',...
    'MaxEpochs',250,...
    'MiniBatchSize',27,...
    'GradientThreshold',1,...
    'Verbose',false,...
    'Plots','training-progress');
net=trainNetwork(x_train',y_train',layer,options);

i have this error: Invalid training data. The output size (1) of the last layer does not match the response size (2250).

[KSSV]

You need to attach your input data. Attach the mat file.

[David Vatavu]

Are you sure? What do you think about creating the neural network?

Answer 1

[Gagan Agarwal]

Hi David,

I understand that you are encountering an error that the output size of last layer does not match with the response size.

To address this issue, it is important to ensure that the output size of the last layer matches the size of your training output data. Please refer to the following approaches to resolve the issue:

1. Review the dimensions of your training data and the expected output size. There might be inconsistency in dimensions between training data and the expected output size
2. Verify the architecture of your neural network and the dimensions of each layer, especially the last layer. Ensure that the output size of the last layer matches the expected response size.

I hope it helps!