When developing a neural network application, it is quite common to face problems regarding how accurate the results are. The source of these problems can be various:
bad input selection
noisy data
very big dataset
unsuitable structure
inadequate number of hidden neurons
inadequate learning rate
insufficient stop condition; and/or
bad dataset segmentation
The design of a neural network application sometimes requires a lot of patience and trial-and-error methods. There is no methodology stating specifically the number of hidden units and/or which architecture should be used, but there are recommendations on how to properly choose these parameters. Another issue that programmers may face is a long training time, which often causes the neural network to not learn the data. No matter how long the training runs, the neural network won't converge.