Neural Network training doesn't yield good results

Hi, I'm trying to use a neural network for a classification problem, but the result of the training produce very bad performance. The classification problem:

• I have more than 300,000 training samples
• Each input is a vector of 32 values (real values)
• Each output is a vector of 32 values (0 or 1)

This is how I train the network:

DNN_SIZE = [1000, 1000];
% Initialize DNN
net = feedforwardnet(DNN_SIZE, 'traingda');
net.performParam.regularization = 0.2;

%Set activation functions
for i=1:length(DNN_SIZE)
    net.layers{i}.transferFcn = 'poslin';
end
net.layers{end}.transferFcn = 'logsig';

net = train(net, train_inputs, train_outputs);

• Note: I have tried different values for DNN_SIZE including larger and smaller values, for hidden layers and less, but it didn't make a difference.
• Note 2: I have tried training the same network using a data set from Matlab's examples (simpleclass_dataset) and I still got bad performance.

The performance of the trained network is very bad- Its output is basically 0.5 in every output for every input vector (when the target outputs during training are always 0 or 1). What am I doing wrong, and how can I fix it?

Thanks.

[Walter Roberson]

What reason do you have to expect that you can do better than 50 percent?

I could use rand() to create a 300000 by 32 array of coefficients and randomly assign them to targets 0 and 1, but clearly any classification that did better than 50 percent could only do so through overclassification. What evidence is available to you to suggest that your data can do better?