Inputing Values to ANN Matlab model

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;

[ahtesham Khizer]

Plz i also dont get the outputvalues by assigning input value plz mention command here thanks.

Answer 1

[Abhishek Gupta]

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

[Jack Durkacz]

Thank you. I have managed to solve the issue now.