why does I-H-O neural network predict constant values?

Ingrid

2015 2 25

0 个回答

10 次查看（30 天）

0 个投票

my neural network was working reasonably, except for the prediction of negative values sometimes which is impossible physically since I am trying to predict concentrations in the sewer system based on 5 relevant parameters. Therefore, I tried the suggestion proposed by Greg in the following thread:

http://www.mathworks.com/matlabcentral/answers/158570-how-can-i-avoid-negative-values-in-the-output-of-a-feedforward-net

namely "Use 'tansig' for hidden and 'logsig' for output."

however, when I implement this, all the predicted values go to a constant value (which is about halve of the maximum) and the optimizaion process stops. I thought it might be a problem of rescaling my data (maximum is now around 300), but after rescaling such that the maximum is one, the problem still remains, now converging all results to 0.5

what am I doing wrong? Is there another possibility of making sure my NN does not predict negative values?

hiddenLayerSize = 20;
net = fitnet(hiddenLayerSize);
net.layers{1}.transferFcn = 'tansig'; % hidden layer
net.layers{2}.transferFcn = 'logsig'; % output layer
[net,tr] = train(net,inputs,targets);

5 个评论
显示 3更早的评论隐藏 3更早的评论

Ingrid 2015-7-30

still nobody that has an idea on how to solve this? The problem is still persisting and I really would have liked to have a working NN in my results...