Description
I have 3 inputs and 12 outputs data. My programme need to be recognize the pattern from existing data, after that need to predict suitable outputs for new inputs. Every data have different range. Input (IP) 01 between 0.1 to 3, IP 02 between 1 to 8, IP 03 between 1 to 5, for first ten outputs (OP) between 0 to 0.25, 11th OP between 0 to 6, 12th OP between 0 to 0.15.
Question 01 The neural network start with random weights, the results also slightly differ everytime it is run. How can I control it to same outputs every time?
Question 02 I was in the mid of programming (attached below), what is the function prefer to use here to get the class indices?
>> x=Inputs; >> t=Targets; >> size(x)
ans =
3 100
>> size(t)
ans =
12 100>> net=patternnet(20);>> view(net)>> [net,tr]=train(net,x,t);>> plotperform(tr)>> testX=x(:,tr.testInd);>> testT=t(:,tr.testInd);>> testY=net(testX);>> testIndices=
Best Answer