One customer needed 6 decimal place accuracy on the outputs of the network for a sensor calibration problem. When working with time series, it is convenient to work with a standard size training file. If the training data had to be rebuilt for every test network run, time and disk space would be wasted. This allows the network to use its resources for the more difficult training examples. Input Noise Push the network performance on small training sets to the limit.
Most data files contain noise.
We can never estimate the accuracy of measurements in generating the training data. By adding gaussian noise to each input, we simulate the generation of additional training examples in a noisy environment. Visualization Tools Look inside the net, while it is training, for a better understanding of what and how the network is learning. With Kohonen learning, for example, the weights become prototypes or templates of the training data. In supervised learning, see what features the network uses in the hidden processing elements.