Inputing Values to ANN Matlab model

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Jack Durkacz
Jack Durkacz 2021-2-13
评论： ahtesham Khizer 2022-9-17
I have been able to succesfully train my ann model. I now would like to give the ann model new inputs for it to predict the values. I can then compare the ANN prediction for this data set with the known. So far I have tried many options but dont seem to be getting the right answers. My code is below.
I have tried results = net(newinputs) and tried un normalising the results nut no joy. Am I barking up the wrong tree with this?
Thanks.
data = readmatrix('VAWT.csv');
x = data(:,1:4);
y = data(:,5)
m = length(y);
Visulisation of the data
histogram(x(:,4),10);
plot(x(:,4),y,'o')
Normalise the features and transform the output
y2 = log(abs(y+1));
for i = 1:4
x2(:,i) = (x(:,i)-min(x(:,i)))/(max(x(:,i))-min(x(:,i)))
end
histogram(x2(:,4),10);
plot(x2(:,1),y2,'o');
Train an Aritificial neural network (ANN)
xt = x2';
yt = y2';
hiddenLayerSize = 7;
net = fitnet(hiddenLayerSize);
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 30/100;
net.divideParam.testRatio = 0/100;
[net,tr] = train(net, xt, yt);
Performance of the ANN Network
yTrain = exp(net(xt(:,tr.trainInd)))-1;
yTrainTrue = exp(yt(tr.trainInd))-1;
sqrt(mean((yTrain - yTrainTrue).^2))
yVal = exp(net(xt(:,tr.valInd)))-1;
yValTrue = exp(yt(tr.valInd))-1;
sqrt(mean((yVal - yValTrue).^2))
Visualize the predictions from the ANN model
plot(yTrainTrue,yTrain,'x'); hold on;
plot(yValTrue,yVal,'o');
plot(0:40,0:40); hold off;
Optimize the number of neurons in the hidden layer
for i = 1:60
% defining the architecture of the ANN
hiddenLayerSize = i;
net = fitnet(hiddenLayerSize);
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 0/100;
% training the ANN
[net,tr] = train(net, xt, yt);
% determine the error of the ANN
yTrain = exp(net(xt(:,tr.trainInd)))-1;
yValTrue = exp(yt(tr.valInd))-1;
yTrainTrue = exp(yt(tr.trainInd))-1;
yVal = exp(net(xt(:,tr.valInd)))-1;
rmse_train(i) = sqrt(mean((yTrain - yTrainTrue).^2)) % RMSE of training
rmse_val(i) = sqrt(mean((yVal - yValTrue).^2)) % RMSE of validation set
end
Select the optimal number of Neurons in the hidden layer
plot(1:60,rmse_train); hold on;
plot(1:60,rmse_val); hold off;
  1 个评论
ahtesham Khizer
ahtesham Khizer 2022-9-17
Plz i also dont get the outputvalues by assigning input value plz mention command here thanks.

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采纳的回答

Abhishek Gupta
Abhishek Gupta 2021-2-16
Hi,
As per my understanding, you want to make predictions for new input using your trained network. You can do the same using the 'predict()' function in MATLAB as follows: -
YPred = predict(net,XTest);
For more information, refer to the following documentation link: -
https://www.mathworks.com/help/deeplearning/ref/seriesnetwork.predict.html

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